diff --git a/llvm/include/llvm/Analysis/InlineAdvisor.h b/llvm/include/llvm/Analysis/InlineAdvisor.h
--- a/llvm/include/llvm/Analysis/InlineAdvisor.h
+++ b/llvm/include/llvm/Analysis/InlineAdvisor.h
@@ -41,7 +41,7 @@
 /// training.
 ///
 /// - Dynamically load an advisor via a plugin (PluginInlineAdvisorAnalysis)
-enum class InliningAdvisorMode : int { Default, Release, Development };
+enum class InliningAdvisorMode : int { Default, Release, Development, EmitC };
 
 // Each entry represents an inline driver.
 enum class InlinePass : int {
@@ -356,6 +356,9 @@
                         LazyCallGraph &CG, CGSCCUpdateResult &UR);
 };
 
+std::unique_ptr<InlineAdvisor>
+getEmitCModeAdvisor(Module &M, ModuleAnalysisManager &MAM);
+
 std::unique_ptr<InlineAdvisor>
 getReleaseModeAdvisor(Module &M, ModuleAnalysisManager &MAM);
 
diff --git a/llvm/include/llvm/Analysis/InlineModelFeatureMaps.h b/llvm/include/llvm/Analysis/InlineModelFeatureMaps.h
--- a/llvm/include/llvm/Analysis/InlineModelFeatureMaps.h
+++ b/llvm/include/llvm/Analysis/InlineModelFeatureMaps.h
@@ -22,34 +22,34 @@
 // inline cost, and we define them separately to preserve the original heuristic
 // behavior.
 #define INLINE_COST_FEATURE_ITERATOR(M)                                        \
-  M(SROASavings, "sroa_savings")                                               \
-  M(SROALosses, "sroa_losses")                                                 \
-  M(LoadElimination, "load_elimination")                                       \
-  M(CallPenalty, "call_penalty")                                               \
-  M(CallArgumentSetup, "call_argument_setup")                                  \
-  M(LoadRelativeIntrinsic, "load_relative_intrinsic")                          \
-  M(LoweredCallArgSetup, "lowered_call_arg_setup")                             \
-  M(IndirectCallPenalty, "indirect_call_penalty")                              \
-  M(JumpTablePenalty, "jump_table_penalty")                                    \
-  M(CaseClusterPenalty, "case_cluster_penalty")                                \
-  M(SwitchPenalty, "switch_penalty")                                           \
-  M(UnsimplifiedCommonInstructions, "unsimplified_common_instructions")        \
-  M(NumLoops, "num_loops")                                                     \
-  M(DeadBlocks, "dead_blocks")                                                 \
-  M(SimplifiedInstructions, "simplified_instructions")                         \
-  M(ConstantArgs, "constant_args")                                             \
-  M(ConstantOffsetPtrArgs, "constant_offset_ptr_args")                         \
-  M(CallSiteCost, "callsite_cost")                                             \
-  M(ColdCcPenalty, "cold_cc_penalty")                                          \
-  M(LastCallToStaticBonus, "last_call_to_static_bonus")                        \
-  M(IsMultipleBlocks, "is_multiple_blocks")                                    \
-  M(NestedInlines, "nested_inlines")                                           \
-  M(NestedInlineCostEstimate, "nested_inline_cost_estimate")                   \
-  M(Threshold, "threshold")
+  M(SROASavings, sroa_savings, "")                                             \
+  M(SROALosses, sroa_losses, "")                                               \
+  M(LoadElimination, load_elimination, "")                                     \
+  M(CallPenalty, call_penalty, "")                                             \
+  M(CallArgumentSetup, call_argument_setup, "")                                \
+  M(LoadRelativeIntrinsic, load_relative_intrinsic, "")                        \
+  M(LoweredCallArgSetup, lowered_call_arg_setup, "")                           \
+  M(IndirectCallPenalty, indirect_call_penalty, "")                            \
+  M(JumpTablePenalty, jump_table_penalty, "")                                  \
+  M(CaseClusterPenalty, case_cluster_penalty, "")                              \
+  M(SwitchPenalty, switch_penalty, "")                                         \
+  M(UnsimplifiedCommonInstructions, unsimplified_common_instructions, "")      \
+  M(NumLoops, num_loops, "")                                                   \
+  M(DeadBlocks, dead_blocks, "")                                               \
+  M(SimplifiedInstructions, simplified_instructions, "")                       \
+  M(ConstantArgs, constant_args, "")                                           \
+  M(ConstantOffsetPtrArgs, constant_offset_ptr_args, "")                       \
+  M(CallSiteCost, callsite_cost, "")                                           \
+  M(ColdCcPenalty, cold_cc_penalty, "")                                        \
+  M(LastCallToStaticBonus, last_call_to_static_bonus, "")                      \
+  M(IsMultipleBlocks, is_multiple_blocks, "")                                  \
+  M(NestedInlines, nested_inlines, "")                                         \
+  M(NestedInlineCostEstimate, nested_inline_cost_estimate, "")                 \
+  M(Threshold, threshold, "")
 
 // clang-format off
 enum class InlineCostFeatureIndex : size_t {
-#define POPULATE_INDICES(INDEX_NAME, NAME) INDEX_NAME,
+#define POPULATE_INDICES(INDEX_NAME, NAME, DOC) INDEX_NAME,
   INLINE_COST_FEATURE_ITERATOR(POPULATE_INDICES)
 #undef POPULATE_INDICES
 
@@ -81,39 +81,37 @@
 // programmatically, and serves as workaround to inability of inserting comments
 // in macros.
 #define INLINE_FEATURE_ITERATOR(M)                                             \
-  M(CalleeBasicBlockCount, "callee_basic_block_count",                         \
+  M(CalleeBasicBlockCount, callee_basic_block_count,                           \
     "number of basic blocks of the callee")                                    \
-  M(CallSiteHeight, "callsite_height",                                         \
+  M(CallSiteHeight, callsite_height,                                           \
     "position of the call site in the original call graph - measured from "    \
     "the farthest SCC")                                                        \
-  M(NodeCount, "node_count",                                                   \
+  M(NodeCount, node_count,                                                     \
     "total current number of defined functions in the module")                 \
-  M(NrCtantParams, "nr_ctant_params",                                          \
+  M(NrCtantParams, nr_ctant_params,                                            \
     "number of parameters in the call site that are constants")                \
-  M(CostEstimate, "cost_estimate", "total cost estimate (threshold - free)")   \
-  M(EdgeCount, "edge_count", "total number of calls in the module")            \
-  M(CallerUsers, "caller_users",                                               \
+  M(CostEstimate, cost_estimate, "total cost estimate (threshold - free)")     \
+  M(EdgeCount, edge_count, "total number of calls in the module")              \
+  M(CallerUsers, caller_users,                                                 \
     "number of module-internal users of the caller, +1 if the caller is "      \
     "exposed externally")                                                      \
-  M(CallerConditionallyExecutedBlocks, "caller_conditionally_executed_blocks", \
+  M(CallerConditionallyExecutedBlocks, caller_conditionally_executed_blocks,   \
     "number of blocks reached from a conditional instruction, in the caller")  \
-  M(CallerBasicBlockCount, "caller_basic_block_count",                         \
+  M(CallerBasicBlockCount, caller_basic_block_count,                           \
     "number of basic blocks in the caller")                                    \
-  M(CalleeConditionallyExecutedBlocks, "callee_conditionally_executed_blocks", \
+  M(CalleeConditionallyExecutedBlocks, callee_conditionally_executed_blocks,   \
     "number of blocks reached from a conditional instruction, in the callee")  \
-  M(CalleeUsers, "callee_users",                                               \
+  M(CalleeUsers, callee_users,                                                 \
     "number of module-internal users of the callee, +1 if the callee is "      \
     "exposed externally")
 
 // clang-format off
 enum class FeatureIndex : size_t {
+#define POPULATE_INDICES(INDEX_NAME, NAME, COMMENT) INDEX_NAME,
 // InlineCost features - these must come first
-#define POPULATE_INDICES(INDEX_NAME, NAME) INDEX_NAME,
   INLINE_COST_FEATURE_ITERATOR(POPULATE_INDICES)
-#undef POPULATE_INDICES
 
 // Non-cost features
-#define POPULATE_INDICES(INDEX_NAME, NAME, COMMENT) INDEX_NAME,
   INLINE_FEATURE_ITERATOR(POPULATE_INDICES)
 #undef POPULATE_INDICES
 
diff --git a/llvm/include/llvm/Analysis/MLModelRunner.h b/llvm/include/llvm/Analysis/MLModelRunner.h
--- a/llvm/include/llvm/Analysis/MLModelRunner.h
+++ b/llvm/include/llvm/Analysis/MLModelRunner.h
@@ -47,7 +47,14 @@
     return (const_cast<MLModelRunner *>(this))->getTensorUntyped(Index);
   }
 
-  enum class Kind : int { Unknown, Release, Development, NoOp, Interactive };
+  enum class Kind : int {
+    Unknown,
+    Release,
+    Development,
+    NoOp,
+    Interactive,
+    EmitC
+  };
   Kind getKind() const { return Type; }
   virtual void switchContext(StringRef Name) {}
 
diff --git a/llvm/lib/Analysis/CMakeLists.txt b/llvm/lib/Analysis/CMakeLists.txt
--- a/llvm/lib/Analysis/CMakeLists.txt
+++ b/llvm/lib/Analysis/CMakeLists.txt
@@ -23,6 +23,11 @@
   endif()
 endif()
 
+if (DEFINED LLVM_HAVE_EMITC_INLINERSIZEMODEL)
+  add_compile_definitions(LLVM_HAVE_EMITC_INLINERSIZEMODEL)
+  include_directories(${LLVM_EMITC_RUNTIME_INCLUDE_PATH})
+endif()
+
 add_llvm_component_library(LLVMAnalysis
   AliasAnalysis.cpp
   AliasAnalysisEvaluator.cpp
diff --git a/llvm/lib/Analysis/InlineAdvisor.cpp b/llvm/lib/Analysis/InlineAdvisor.cpp
--- a/llvm/lib/Analysis/InlineAdvisor.cpp
+++ b/llvm/lib/Analysis/InlineAdvisor.cpp
@@ -234,6 +234,9 @@
     LLVM_DEBUG(dbgs() << "Using release-mode inliner policy.\n");
     Advisor = llvm::getReleaseModeAdvisor(M, MAM);
     break;
+  case InliningAdvisorMode::EmitC:
+    LLVM_DEBUG(dbgs() << "Using EmitC-compiled policy.\n");
+    Advisor = llvm::getEmitCModeAdvisor(M, MAM);
   }
 
   return !!Advisor;
diff --git a/llvm/lib/Analysis/MLInlineAdvisor.cpp b/llvm/lib/Analysis/MLInlineAdvisor.cpp
--- a/llvm/lib/Analysis/MLInlineAdvisor.cpp
+++ b/llvm/lib/Analysis/MLInlineAdvisor.cpp
@@ -29,6 +29,7 @@
 #include "llvm/IR/InstIterator.h"
 #include "llvm/IR/PassManager.h"
 #include "llvm/Support/CommandLine.h"
+#include "MLInlinerEmitCRunner.h"
 
 using namespace llvm;
 
@@ -47,6 +48,17 @@
 using CompiledModelType = NoopSavedModelImpl;
 #endif
 
+std::unique_ptr<InlineAdvisor>
+llvm::getEmitCModeAdvisor(Module &M, ModuleAnalysisManager &MAM) {
+#if defined(LLVM_HAVE_EMITC_INLINERSIZEMODEL)
+  auto Runner = std::make_unique<EmitCModelRunner>(M.getContext(), FeatureMap, InlineDecisionSpec);
+  return std::make_unique<MLInlineAdvisor>(M, MAM, std::move(Runner));
+#else
+  M.getContext().emitError("LLVM was not built with LLVM_HAVE_EMITC_INLINERSIZEMODEL -- cannot use the EmitC inliner");
+  return nullptr;
+#endif
+}
+
 std::unique_ptr<InlineAdvisor>
 llvm::getReleaseModeAdvisor(Module &M, ModuleAnalysisManager &MAM) {
   if (!llvm::isEmbeddedModelEvaluatorValid<CompiledModelType>() &&
@@ -80,13 +92,11 @@
 
 // clang-format off
 const std::vector<TensorSpec> llvm::FeatureMap{
-#define POPULATE_NAMES(_, NAME) TensorSpec::createSpec<int64_t>(NAME, {1} ),
+#define POPULATE_NAMES(_, NAME, __) TensorSpec::createSpec<int64_t>(#NAME, {1} ),
 // InlineCost features - these must come first
   INLINE_COST_FEATURE_ITERATOR(POPULATE_NAMES)
-#undef POPULATE_NAMES
 
 // Non-cost features
-#define POPULATE_NAMES(_, NAME, __) TensorSpec::createSpec<int64_t>(NAME, {1} ),
   INLINE_FEATURE_ITERATOR(POPULATE_NAMES)
 #undef POPULATE_NAMES
 };
diff --git a/llvm/lib/Analysis/MLInlinerEmitCRunner.h b/llvm/lib/Analysis/MLInlinerEmitCRunner.h
new file mode 100644
--- /dev/null
+++ b/llvm/lib/Analysis/MLInlinerEmitCRunner.h
@@ -0,0 +1,77 @@
+//===- MLInlinerEmitCRunner.h ---- EmitC ML model runner  -------*- C++ -*-===//
+//
+// Part of the LLVM Project, under the Apache License v2.0 with LLVM Exceptions.
+// See https://llvm.org/LICENSE.txt for license information.
+// SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
+//
+//===----------------------------------------------------------------------===//
+//
+
+#ifndef LLVM_ANALYSIS_EMITCMODELRUNNER_H
+#define LLVM_ANALYSIS_EMITCMODELRUNNER_H
+
+#if defined(LLVM_HAVE_EMITC_INLINERSIZEMODEL)
+
+#include "llvm/Analysis/MLModelRunner.h"
+#include "llvm/Analysis/TensorSpec.h"
+
+#include <emitc/types.h>
+
+#include "models/emitc/MLInlinerSizeModel.h"
+#include "llvm/Analysis/InlineModelFeatureMaps.h"
+
+namespace llvm {
+
+template <class T, int64_t... Dims>
+Tensor<T, Dims...> convertBufferToEmitCTensor(void *Buffer, TensorSpec Spec) {
+  std::vector<T> Data;
+  T *TypedBuffer = static_cast<T *>(Buffer);
+  std::copy(TypedBuffer, TypedBuffer + Spec.getElementCount(),
+            std::back_inserter(Data));
+  return Tensor<T, Dims...>(std::move(Data));
+}
+
+class EmitCModelRunner : public MLModelRunner {
+public:
+  EmitCModelRunner(LLVMContext &Ctx, const std::vector<TensorSpec> &Inputs,
+                   const TensorSpec &Advice)
+      : MLModelRunner(Ctx, MLModelRunner::Kind::EmitC, Inputs.size()),
+        InputSpecs(Inputs), OutputSpec(Advice) {
+    for (size_t I = 0; I < Inputs.size(); ++I) {
+      setUpBufferForTensor(I, Inputs[I], nullptr);
+    }
+  }
+
+  static bool classof(const MLModelRunner *R) {
+    return R->getKind() == MLModelRunner::Kind::EmitC;
+  }
+
+  virtual ~EmitCModelRunner() {}
+
+  void *evaluateUntyped() override {
+#define FEATURE_SETTER(cpp_name, py_name, _)                                   \
+  do {                                                                         \
+    size_t idx = static_cast<size_t>(FeatureIndex::cpp_name);                  \
+    Model.set_##py_name(convertBufferToEmitCTensor<int64_t, 1>(                \
+        getTensorUntyped(idx), InputSpecs[idx]));                              \
+  } while (false);
+
+    INLINE_FEATURE_ITERATOR(FEATURE_SETTER);
+    INLINE_COST_FEATURE_ITERATOR(FEATURE_SETTER);
+#undef FEATURE_SETTER
+    Result = Model.action();
+    return static_cast<void *>(Result.get());
+  }
+
+private:
+  const std::vector<TensorSpec> InputSpecs;
+  const TensorSpec OutputSpec;
+
+  MLInlineModel Model;
+  Tensor<int64_t, 1> Result;
+};
+
+} // namespace llvm
+
+#endif // defined(LLVM_HAVE_EMITC_INLINERSIZEMODEL)
+#endif
diff --git a/llvm/lib/Analysis/models/emitc/MLInlinerSizeModel.h b/llvm/lib/Analysis/models/emitc/MLInlinerSizeModel.h
new file mode 100644
--- /dev/null
+++ b/llvm/lib/Analysis/models/emitc/MLInlinerSizeModel.h
@@ -0,0 +1,19761 @@
+#include <cstdint>
+
+#include <emitc/tosa.h>
+#include <emitc/types.h>
+
+class MLInlineModel {
+private:
+  Tensor<int64_t, 1> v1;
+  Tensor<int64_t, 1> v2;
+  Tensor<int64_t, 1> v3;
+  Tensor<int64_t, 1> v4;
+  Tensor<int64_t, 1> v5;
+  Tensor<int64_t, 1> v6;
+  Tensor<int64_t, 1> v7;
+  Tensor<int64_t, 1> v8;
+  Tensor<int64_t, 1> v9;
+  Tensor<int64_t, 1> v10;
+  Tensor<int64_t, 1> v11;
+  Tensor<int64_t, 1> v12;
+  Tensor<int64_t, 1> v13;
+  Tensor<int64_t, 1> v14;
+  Tensor<int64_t, 1> v15;
+  Tensor<int64_t, 1> v16;
+  Tensor<int64_t, 1> v17;
+  Tensor<int64_t, 1> v18;
+  Tensor<int64_t, 1> v19;
+  Tensor<int64_t, 1> v20;
+  Tensor<int64_t, 1> v21;
+  Tensor<int64_t, 1> v22;
+  Tensor<int64_t, 1> v23;
+  Tensor<int64_t, 1> v24;
+  Tensor<int64_t, 1> v25;
+  Tensor<int64_t, 1> v26;
+  Tensor<int64_t, 1> v27;
+  Tensor<int32_t, 1> v28;
+  Tensor<int64_t, 1> v29;
+  Tensor<int64_t, 1> v30;
+  Tensor<int64_t, 1> v31;
+  Tensor<int64_t, 1> v32;
+  Tensor<int64_t, 1> v33;
+  Tensor<int64_t, 1> v34;
+  Tensor<int64_t, 1> v35;
+  Tensor<int64_t, 1> v36;
+  Tensor<float, 1> v37;
+  Tensor<int64_t, 1> v38;
+  Tensor<float, 1> v39;
+
+public:
+  void set_callsite_cost(Tensor<int64_t, 1> x) { v1 = std::move(x); }
+  void set_is_multiple_blocks(Tensor<int64_t, 1> x) { v2 = std::move(x); }
+  void set_caller_conditionally_executed_blocks(Tensor<int64_t, 1> x) {
+    v3 = std::move(x);
+  }
+  void set_inlining_default(Tensor<int64_t, 1> x) { v4 = std::move(x); }
+  void set_cold_cc_penalty(Tensor<int64_t, 1> x) { v5 = std::move(x); }
+  void set_callee_conditionally_executed_blocks(Tensor<int64_t, 1> x) {
+    v6 = std::move(x);
+  }
+  void set_callee_users(Tensor<int64_t, 1> x) { v7 = std::move(x); }
+  void set_callee_basic_block_count(Tensor<int64_t, 1> x) { v8 = std::move(x); }
+  void set_nr_ctant_params(Tensor<int64_t, 1> x) { v9 = std::move(x); }
+  void set_load_relative_intrinsic(Tensor<int64_t, 1> x) { v10 = std::move(x); }
+  void set_jump_table_penalty(Tensor<int64_t, 1> x) { v11 = std::move(x); }
+  void set_unsimplified_common_instructions(Tensor<int64_t, 1> x) {
+    v12 = std::move(x);
+  }
+  void set_indirect_call_penalty(Tensor<int64_t, 1> x) { v13 = std::move(x); }
+  void set_load_elimination(Tensor<int64_t, 1> x) { v14 = std::move(x); }
+  void set_call_penalty(Tensor<int64_t, 1> x) { v15 = std::move(x); }
+  void set_cost_estimate(Tensor<int64_t, 1> x) { v16 = std::move(x); }
+  void set_case_cluster_penalty(Tensor<int64_t, 1> x) { v17 = std::move(x); }
+  void set_node_count(Tensor<int64_t, 1> x) { v18 = std::move(x); }
+  void set_call_argument_setup(Tensor<int64_t, 1> x) { v19 = std::move(x); }
+  void set_sroa_savings(Tensor<int64_t, 1> x) { v20 = std::move(x); }
+  void set_lowered_call_arg_setup(Tensor<int64_t, 1> x) { v21 = std::move(x); }
+  void set_threshold(Tensor<int64_t, 1> x) { v22 = std::move(x); }
+  void set_dead_blocks(Tensor<int64_t, 1> x) { v23 = std::move(x); }
+  void set_constant_args(Tensor<int64_t, 1> x) { v24 = std::move(x); }
+  void set_sroa_losses(Tensor<int64_t, 1> x) { v25 = std::move(x); }
+  void set_simplified_instructions(Tensor<int64_t, 1> x) { v26 = std::move(x); }
+  void set_num_loops(Tensor<int64_t, 1> x) { v27 = std::move(x); }
+  void set_step_type(Tensor<int32_t, 1> x) { v28 = std::move(x); }
+  void set_edge_count(Tensor<int64_t, 1> x) { v29 = std::move(x); }
+  void set_nested_inlines(Tensor<int64_t, 1> x) { v30 = std::move(x); }
+  void set_caller_basic_block_count(Tensor<int64_t, 1> x) {
+    v31 = std::move(x);
+  }
+  void set_last_call_to_static_bonus(Tensor<int64_t, 1> x) {
+    v32 = std::move(x);
+  }
+  void set_nested_inline_cost_estimate(Tensor<int64_t, 1> x) {
+    v33 = std::move(x);
+  }
+  void set_callsite_height(Tensor<int64_t, 1> x) { v34 = std::move(x); }
+  void set_constant_offset_ptr_args(Tensor<int64_t, 1> x) {
+    v35 = std::move(x);
+  }
+  void set_switch_penalty(Tensor<int64_t, 1> x) { v36 = std::move(x); }
+  void set_discount(Tensor<float, 1> x) { v37 = std::move(x); }
+  void set_caller_users(Tensor<int64_t, 1> x) { v38 = std::move(x); }
+  void set_reward(Tensor<float, 1> x) { v39 = std::move(x); }
+  Tensor<int64_t, 1> action() {
+    Tensor<float, 1, 1> v40 = {(float)1.001000990e-03};
+    Tensor<int64_t, 1, 1, 999> v41 = {
+        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
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+        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0};
+    Tensor<float, 1, 1> v42 = {(float)1.000100020e-04};
+    Tensor<int64_t, 1> v43 = {1};
+    Tensor<int64_t, 1> v44 = {0};
+    Tensor<float, 999> v45 = {
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+        (float)-1.238144110e-01, (float)-1.419138910e+00,
+        (float)-2.494237870e-01, (float)6.256981940e-02,
+        (float)3.646745680e-01,  (float)-2.165383250e-01,
+        (float)2.244830880e-01,  (float)2.276125850e-01,
+        (float)-1.850101200e-01, (float)2.718917730e-01,
+        (float)-2.323683950e-01, (float)4.239770470e-01,
+        (float)4.413184520e-01,  (float)5.479641560e-01,
+        (float)5.835102870e-02,  (float)1.627402160e-01,
+        (float)3.984292450e-01,  (float)-5.058600310e-01,
+        (float)6.110152230e-03,  (float)2.891739010e-01,
+        (float)2.458074840e-01,  (float)4.677050190e-02,
+        (float)3.887647760e-02,  (float)1.007662790e-01,
+        (float)1.478621360e-01,  (float)-2.735628000e-02,
+        (float)-2.269560840e-01, (float)-6.399090590e-02,
+        (float)-1.326396020e-01, (float)1.389522110e-01,
+        (float)1.239052190e-01,  (float)-1.332591920e-01,
+        (float)-3.580741880e-01, (float)4.372794030e-01,
+        (float)-6.367521730e-02, (float)7.873125370e-02,
+        (float)-3.236582580e-01, (float)3.613115850e-01,
+        (float)-6.108172530e-01, (float)-1.694088130e-01,
+        (float)5.440499190e-01,  (float)2.051893320e-01,
+        (float)5.313488100e-02,  (float)-3.597162370e-01,
+        (float)-2.201606040e-01, (float)2.166824940e-01,
+        (float)2.179640380e-01,  (float)-3.193285760e-01,
+        (float)-1.254470500e-01, (float)-3.621170520e-01,
+        (float)6.087042090e-01,  (float)-7.966090440e-01,
+        (float)-3.799393480e-01, (float)-2.221991120e-01,
+        (float)-3.973305520e-01, (float)-9.156332910e-02,
+        (float)-2.928413150e-01, (float)1.620428410e-01,
+        (float)3.589529990e-01,  (float)-5.532604460e-01,
+        (float)-6.577098370e-01, (float)-1.049700450e-01,
+        (float)-2.303448920e-01, (float)-2.207996990e-01,
+        (float)-3.963633630e-02, (float)6.776762010e-02,
+        (float)2.980679270e-01,  (float)3.605761530e-01,
+        (float)2.977470160e-01,  (float)3.060776890e-01,
+        (float)1.379473360e-01,  (float)8.219244330e-02,
+        (float)2.234071680e-02,  (float)-1.193575930e-01,
+        (float)3.796038630e-01,  (float)-3.785829540e-01,
+        (float)4.707578120e-01,  (float)-2.772249280e-01,
+        (float)4.983121160e-01,  (float)3.462522920e-01,
+        (float)-3.079601190e-02, (float)-1.563043000e+00,
+        (float)1.675915420e-01,  (float)5.999301080e-01,
+        (float)-3.162841870e-02, (float)2.056206020e-01,
+        (float)-4.244134430e-01, (float)4.279303850e-01,
+        (float)-3.854548630e-01, (float)3.580687940e-01,
+        (float)-3.587054910e-01, (float)3.298999970e-01,
+        (float)5.193746090e-01,  (float)7.040132880e-01,
+        (float)3.866367790e-02,  (float)2.019271440e-02,
+        (float)4.187961220e-01,  (float)-3.140890600e-01,
+        (float)-2.354049240e-01, (float)-2.892868130e-03,
+        (float)2.037613540e-01,  (float)3.111617270e-01,
+        (float)3.128022700e-02,  (float)-2.649013100e-01,
+        (float)-1.832012390e-01, (float)-1.581656630e-01,
+        (float)-6.082282590e-02, (float)-2.122654170e-01,
+        (float)-4.257928430e-01, (float)-3.733527360e-01,
+        (float)2.263738210e-02,  (float)-2.088063210e-01,
+        (float)-4.577190580e-01, (float)1.960593310e-01,
+        (float)1.726151440e-02,  (float)1.379688380e-01,
+        (float)3.682704870e-01,  (float)1.445295070e-01,
+        (float)4.831479790e-01,  (float)-1.794906260e-01,
+        (float)3.627341690e-01,  (float)-1.277740120e+00,
+        (float)-5.864397880e-01, (float)-9.733753270e-03,
+        (float)2.080687550e-03,  (float)-1.437726320e-01,
+        (float)5.880901590e-02,  (float)6.388892530e-01,
+        (float)-3.990408780e-01, (float)4.481624070e-01,
+        (float)-1.446704270e-01, (float)2.488772380e-02,
+        (float)6.125096080e-01,  (float)2.471455480e-01,
+        (float)-1.556656210e-01, (float)4.514379200e-01,
+        (float)3.413251640e-01,  (float)3.209660350e-01,
+        (float)-2.382310030e-01, (float)3.141963780e-01,
+        (float)3.679513930e-01,  (float)1.523191480e-01,
+        (float)-6.320298180e-03, (float)4.216961560e-01,
+        (float)-6.966105850e-02, (float)-5.016719700e-01,
+        (float)1.266679320e-01,  (float)-4.081384540e-01,
+        (float)-9.559302770e-02, (float)3.579595690e-01,
+        (float)9.735561160e-02,  (float)-4.430008530e-01,
+        (float)-8.781628310e-02, (float)-1.213255300e-01,
+        (float)-3.240779040e-01, (float)4.279552100e-01,
+        (float)-4.476383030e-01, (float)5.918712170e-02,
+        (float)-3.643892410e-01, (float)-4.493504760e-02,
+        (float)4.639157060e-01,  (float)1.894602330e-01,
+        (float)-3.626428170e-02, (float)-4.158381520e-01,
+        (float)-3.826033170e-01, (float)4.321651160e-01,
+        (float)2.226953210e-02,  (float)-5.559861660e-01,
+        (float)-2.112814780e-01, (float)-3.501964730e-02,
+        (float)1.636847260e-01,  (float)-4.629981220e-01,
+        (float)5.134687950e-02,  (float)-2.167737480e-01,
+        (float)-2.672149240e-01, (float)1.088453550e-02,
+        (float)-5.553131700e-01, (float)2.548821570e-01,
+        (float)3.301298320e-01,  (float)-5.643733140e-01,
+        (float)-3.154408930e-01, (float)-3.415465950e-01,
+        (float)-7.611398400e-02, (float)-1.689970200e-01,
+        (float)1.680674250e-01,  (float)-1.558790950e-01,
+        (float)4.478333590e-01,  (float)1.536211820e-01,
+        (float)5.452746150e-01,  (float)1.757065500e-01,
+        (float)-4.403058810e-02, (float)1.394289730e-01,
+        (float)4.948657750e-01,  (float)5.768773560e-01,
+        (float)-2.722059190e-01, (float)5.695849060e-01,
+        (float)-4.183717670e-01, (float)2.392334640e-01,
+        (float)-2.316315470e-01, (float)-2.036043850e-01,
+        (float)2.217260000e-01,  (float)1.366036090e-01,
+        (float)1.578269600e-01,  (float)-9.047125280e-02,
+        (float)-7.669185100e-02, (float)5.522924070e-01,
+        (float)-3.903945680e-01, (float)-5.607128140e-01,
+        (float)8.497425910e-02,  (float)-2.864985470e-01,
+        (float)4.412244560e-01,  (float)-2.547703980e-01,
+        (float)-5.551274870e-03, (float)-2.311520430e-01,
+        (float)-1.810989980e-01, (float)-8.524540060e-02,
+        (float)-5.332911010e-01, (float)1.067483350e-01,
+        (float)-1.229953690e-01, (float)-2.399736490e-01,
+        (float)-2.057684400e-02, (float)-1.616915320e-01,
+        (float)-2.550775410e-01, (float)-1.832204310e-01,
+        (float)2.227985110e-01,  (float)2.124263050e-01,
+        (float)2.464320810e-01,  (float)1.240458040e-01,
+        (float)-8.670216050e-02, (float)-4.733902590e-02,
+        (float)-3.029606340e-01, (float)2.670714260e-01,
+        (float)9.264408790e-02,  (float)5.991507770e-01,
+        (float)-4.665857260e-01, (float)-2.562563720e-01,
+        (float)-4.856235980e-01, (float)3.031614420e-01,
+        (float)-3.425578180e-01, (float)-2.477878630e-01,
+        (float)5.223798160e-01,  (float)-8.202544590e-02,
+        (float)1.909095050e-01,  (float)-9.334887260e-01,
+        (float)-5.372853880e-01, (float)8.703056720e-02,
+        (float)-5.053129200e-01, (float)-5.121751430e-01,
+        (float)4.443113210e-01,  (float)-6.320797800e-01,
+        (float)5.291313530e-01,  (float)-6.553611150e-01,
+        (float)-2.686398630e-01, (float)-8.193420610e-02,
+        (float)-6.151188010e-01, (float)-4.099730250e-01,
+        (float)-8.539401730e-02, (float)-2.664578560e-01,
+        (float)-1.852691770e-01, (float)-5.968403220e-01,
+        (float)-4.007101360e-01, (float)-5.799773930e-01,
+        (float)-6.878847470e-01, (float)-5.377997760e-01,
+        (float)-6.975211950e-02, (float)1.823332910e-01,
+        (float)-7.733856890e-02, (float)4.465521280e-01,
+        (float)5.723667140e-02,  (float)1.903191060e-01,
+        (float)-2.313067320e-01, (float)2.883432510e-01,
+        (float)7.287859170e-02,  (float)9.792231020e-02,
+        (float)-1.277662070e-01, (float)3.055700660e-02,
+        (float)-5.965122210e-02, (float)2.636531590e-01,
+        (float)-4.168261590e-01, (float)-3.450688120e-01,
+        (float)2.628262040e-01,  (float)-3.421325090e-01,
+        (float)1.955952940e-01,  (float)-3.614782390e-01,
+        (float)-2.572630640e-01, (float)3.130463360e-01,
+        (float)1.620394140e-01,  (float)-5.128130910e-01,
+        (float)-8.077862110e-02, (float)-2.193410550e-01,
+        (float)3.097976740e-01,  (float)-4.309728150e-01,
+        (float)-1.454005540e-01, (float)8.374135200e-02,
+        (float)-1.273479010e-01, (float)-1.326998170e-01,
+        (float)-4.173532720e-01, (float)2.786041500e-01,
+        (float)3.906883000e-01,  (float)-3.440140780e-01,
+        (float)-1.630935370e-01, (float)-1.279881300e-01,
+        (float)-2.115882780e-01, (float)-2.303743810e-01,
+        (float)1.683009860e-01,  (float)4.120436610e-01,
+        (float)2.626100480e-01,  (float)2.778919640e-01,
+        (float)3.377864060e-01,  (float)8.225002880e-02,
+        (float)1.469960960e-01,  (float)-1.747772840e-01,
+        (float)3.403380810e-01,  (float)2.568980160e-01,
+        (float)-2.144733520e-01, (float)4.541271630e-01,
+        (float)-1.794124250e-01, (float)1.637876180e-01,
+        (float)-2.590546010e-01, (float)-6.642739770e-01,
+        (float)3.656847180e-01,  (float)1.298068760e-01,
+        (float)5.232693630e-02,  (float)-4.279178080e-01,
+        (float)-2.851843830e-01, (float)1.670745610e-01,
+        (float)-4.303287860e-01, (float)-4.079115390e-01,
+        (float)4.602125290e-01,  (float)-5.974282620e-01,
+        (float)1.458881200e-01,  (float)-6.717157360e-01,
+        (float)-6.618970630e-01, (float)5.428814790e-03,
+        (float)-4.026044610e-01, (float)-2.305841890e-01,
+        (float)-7.072991130e-01, (float)-1.171246100e-01,
+        (float)2.452067140e-01,  (float)-2.774640620e-01,
+        (float)-1.671426300e-01, (float)-4.060022230e-01,
+        (float)-4.236065450e-01, (float)-5.231152180e-01,
+        (float)2.592145840e-02,  (float)6.514772180e-01,
+        (float)-7.078727240e-03, (float)4.641992750e-01,
+        (float)3.235935870e-01,  (float)-1.293828490e-01,
+        (float)-2.209510210e-01, (float)1.755362000e-01,
+        (float)2.179742010e-01,  (float)-5.833495780e-02,
+        (float)-8.300729990e-01, (float)5.611345920e-02,
+        (float)-4.962053300e-01, (float)6.439535620e-01,
+        (float)-6.363400220e-01, (float)-8.765996090e-01,
+        (float)5.768699200e-02,  (float)1.422120930e-01,
+        (float)9.066201750e-04,  (float)-7.595893140e-01,
+        (float)-8.425087330e-01, (float)6.453705430e-01,
+        (float)-3.015207350e-01, (float)-5.132936840e-01,
+        (float)2.942183320e-01,  (float)-6.713371870e-01,
+        (float)3.467343150e-01,  (float)-8.142403360e-01,
+        (float)-7.736653680e-01, (float)-2.264990810e-01,
+        (float)-6.483533380e-01, (float)-1.229846920e-01,
+        (float)-8.217471830e-01, (float)2.703226510e-01,
+        (float)3.513719140e-02,  (float)-6.510109300e-01,
+        (float)-6.906598810e-01, (float)-3.688707050e-01,
+        (float)-9.579101800e-01, (float)-8.507568840e-01,
+        (float)3.010917310e-01,  (float)1.975249650e-01,
+        (float)7.201362250e-01,  (float)-3.384157720e-01,
+        (float)2.696233690e-01,  (float)-2.802918260e-01,
+        (float)-2.319679410e-01, (float)3.688238260e-01,
+        (float)-2.333278510e-01, (float)-2.279450000e-01,
+        (float)6.323163210e-02,  (float)-1.206192450e-01,
+        (float)6.161751740e-01,  (float)-4.157950280e-01,
+        (float)1.136372310e-01,  (float)1.284490530e-01,
+        (float)-1.384648680e-01, (float)-1.306738380e+00,
+        (float)1.065198780e-01,  (float)2.660389840e-01,
+        (float)1.070073770e-01,  (float)-4.503095150e-02,
+        (float)2.657091620e-01,  (float)4.724887310e-01,
+        (float)-8.700073510e-02, (float)1.551671330e-01,
+        (float)-4.270904360e-01, (float)1.554446670e-01,
+        (float)-1.092808620e-01, (float)2.424404320e-01,
+        (float)3.407474760e-01,  (float)-1.693004070e-01,
+        (float)3.992065190e-01,  (float)-5.221441980e-01,
+        (float)1.327640940e-02,  (float)3.855528530e-01,
+        (float)-1.129992530e-01, (float)5.407613140e-02,
+        (float)2.742832600e-01,  (float)2.237539890e-01,
+        (float)3.193411830e-01,  (float)-4.487538930e-01,
+        (float)-1.554405880e-02, (float)-2.143167260e-01,
+        (float)-1.213760300e-01, (float)1.527040900e-01,
+        (float)1.256113500e-01,  (float)4.726113750e-02};
+    Tensor<float, 20> v80 = {(float)3.355367780e-01,  (float)-1.388000880e-02,
+                             (float)6.751911040e-01,  (float)2.667974220e-03,
+                             (float)1.055345460e-01,  (float)3.203891220e-03,
+                             (float)6.174200770e-01,  (float)-1.178330560e-02,
+                             (float)-1.767182650e-01, (float)5.671358110e-02,
+                             (float)4.362215700e-01,  (float)-1.028030290e-01,
+                             (float)9.947001180e-02,  (float)5.265628100e-01,
+                             (float)6.506712440e-01,  (float)4.370287660e-01,
+                             (float)7.621463530e-01,  (float)5.977262850e-01,
+                             (float)4.100183840e-01,  (float)-1.327016650e-01};
+    Tensor<float, 2, 20> v81 = {
+        (float)7.043257950e-01,  (float)-9.608286610e-02,
+        (float)5.232187510e-01,  (float)-1.858599630e-01,
+        (float)-1.736247840e-01, (float)-3.508510590e-01,
+        (float)4.727205340e-01,  (float)-1.409519020e-01,
+        (float)-2.538996040e-01, (float)-1.509172770e-01,
+        (float)4.717685280e-01,  (float)-3.412884770e-01,
+        (float)-1.333482860e-01, (float)5.071479080e-01,
+        (float)5.508543250e-01,  (float)5.913437600e-01,
+        (float)4.606473150e-01,  (float)6.235318180e-01,
+        (float)8.454865210e-01,  (float)-2.929555180e-01,
+        (float)-7.761295430e-01, (float)1.069886090e-01,
+        (float)-5.386613610e-01, (float)1.911667440e-01,
+        (float)7.969756420e-02,  (float)9.220249200e-02,
+        (float)-5.815454130e-01, (float)4.016868770e-02,
+        (float)2.783014180e-01,  (float)1.644356550e-01,
+        (float)-5.277292730e-01, (float)1.197944950e-01,
+        (float)9.721879660e-02,  (float)-4.874985520e-01,
+        (float)-5.873894100e-01, (float)-5.865266320e-01,
+        (float)-3.519758280e-01, (float)-4.026320280e-01,
+        (float)-8.600480560e-01, (float)1.937968730e-01};
+    Tensor<float, 2> v82 = {(float)-4.473917190e-01, (float)-6.821969150e-02};
+    Tensor<int64_t, 999> v83 = emitc::tosa::cast<Tensor<int64_t, 999>>(v73);
+    Tensor<int64_t, 1, 1, 1> v84 =
+        emitc::tosa::reshape<Tensor<int64_t, 1, 1, 1>>(v38);
+    Tensor<int64_t, 1, 1, 999> v85 =
+        emitc::tosa::reshape<Tensor<int64_t, 1, 1, 999>>(v83);
+    Tensor<int64_t, 1, 1, 999> v86 =
+        emitc::broadcast_in_dim<Tensor<int64_t, 1, 1, 999>>(v84, {0, 1, 2});
+    Tensor<bool, 1, 1, 999> v87 =
+        emitc::tosa::greater_equal<Tensor<bool, 1, 1, 999>>(v86, v85);
+    Tensor<int32_t, 1, 1, 999> v88 =
+        emitc::tosa::cast<Tensor<int32_t, 1, 1, 999>>(v87);
+    Tensor<int32_t, 1, 1> v89 =
+        emitc::tosa::reduce_sum<Tensor<int32_t, 1, 1>,
+                                Tensor<int32_t, 1, 1, 999>>(v88, 2);
+    Tensor<int32_t, 1, 1, 1> v90 =
+        emitc::tosa::reshape<Tensor<int32_t, 1, 1, 1>>(v89);
+    Tensor<int32_t, 1, 1> v91 =
+        emitc::tosa::reshape<Tensor<int32_t, 1, 1>>(v90);
+    Tensor<float, 1, 1> v92 = emitc::tosa::cast<Tensor<float, 1, 1>>(v91);
+    Tensor<float, 1, 1> v93 = emitc::tosa::mul(v92, v40);
+    Tensor<float, 1, 1> v94 = emitc::tosa::mul(v93, v93);
+    Tensor<float, 1, 2> v95 =
+        emitc::tosa::concat<1, Tensor<float, 1, 2>>(v93, v94);
+    Tensor<int64_t, 999> v96 = emitc::tosa::cast<Tensor<int64_t, 999>>(v72);
+    Tensor<int64_t, 1, 1, 1> v97 =
+        emitc::tosa::reshape<Tensor<int64_t, 1, 1, 1>>(v1);
+    Tensor<int64_t, 1, 1, 999> v98 =
+        emitc::tosa::reshape<Tensor<int64_t, 1, 1, 999>>(v96);
+    Tensor<int64_t, 1, 1, 999> v99 =
+        emitc::broadcast_in_dim<Tensor<int64_t, 1, 1, 999>>(v97, {0, 1, 2});
+    Tensor<bool, 1, 1, 999> v100 =
+        emitc::tosa::greater_equal<Tensor<bool, 1, 1, 999>>(v99, v98);
+    Tensor<int32_t, 1, 1, 999> v101 =
+        emitc::tosa::cast<Tensor<int32_t, 1, 1, 999>>(v100);
+    Tensor<int32_t, 1, 1> v102 =
+        emitc::tosa::reduce_sum<Tensor<int32_t, 1, 1>,
+                                Tensor<int32_t, 1, 1, 999>>(v101, 2);
+    Tensor<int32_t, 1, 1, 1> v103 =
+        emitc::tosa::reshape<Tensor<int32_t, 1, 1, 1>>(v102);
+    Tensor<int32_t, 1, 1> v104 =
+        emitc::tosa::reshape<Tensor<int32_t, 1, 1>>(v103);
+    Tensor<float, 1, 1> v105 = emitc::tosa::cast<Tensor<float, 1, 1>>(v104);
+    Tensor<float, 1, 1> v106 = emitc::tosa::mul(v105, v40);
+    Tensor<float, 1, 1> v107 = emitc::tosa::mul(v106, v106);
+    Tensor<float, 1, 2> v108 =
+        emitc::tosa::concat<1, Tensor<float, 1, 2>>(v106, v107);
+    Tensor<int64_t, 999> v109 = emitc::tosa::cast<Tensor<int64_t, 999>>(v71);
+    Tensor<int64_t, 1, 1, 1> v110 =
+        emitc::tosa::reshape<Tensor<int64_t, 1, 1, 1>>(v34);
+    Tensor<int64_t, 1, 1, 999> v111 =
+        emitc::tosa::reshape<Tensor<int64_t, 1, 1, 999>>(v109);
+    Tensor<int64_t, 1, 1, 999> v112 =
+        emitc::broadcast_in_dim<Tensor<int64_t, 1, 1, 999>>(v110, {0, 1, 2});
+    Tensor<bool, 1, 1, 999> v113 =
+        emitc::tosa::greater_equal<Tensor<bool, 1, 1, 999>>(v112, v111);
+    Tensor<int32_t, 1, 1, 999> v114 =
+        emitc::tosa::cast<Tensor<int32_t, 1, 1, 999>>(v113);
+    Tensor<int32_t, 1, 1> v115 =
+        emitc::tosa::reduce_sum<Tensor<int32_t, 1, 1>,
+                                Tensor<int32_t, 1, 1, 999>>(v114, 2);
+    Tensor<int32_t, 1, 1, 1> v116 =
+        emitc::tosa::reshape<Tensor<int32_t, 1, 1, 1>>(v115);
+    Tensor<int32_t, 1, 1> v117 =
+        emitc::tosa::reshape<Tensor<int32_t, 1, 1>>(v116);
+    Tensor<float, 1, 1> v118 = emitc::tosa::cast<Tensor<float, 1, 1>>(v117);
+    Tensor<float, 1, 1> v119 = emitc::tosa::mul(v118, v40);
+    Tensor<float, 1, 1> v120 = emitc::tosa::mul(v119, v119);
+    Tensor<float, 1, 2> v121 =
+        emitc::tosa::concat<1, Tensor<float, 1, 2>>(v119, v120);
+    Tensor<int64_t, 999> v122 = emitc::tosa::cast<Tensor<int64_t, 999>>(v70);
+    Tensor<int64_t, 1, 1, 1> v123 =
+        emitc::tosa::reshape<Tensor<int64_t, 1, 1, 1>>(v17);
+    Tensor<int64_t, 1, 1, 999> v124 =
+        emitc::tosa::reshape<Tensor<int64_t, 1, 1, 999>>(v122);
+    Tensor<int64_t, 1, 1, 999> v125 =
+        emitc::broadcast_in_dim<Tensor<int64_t, 1, 1, 999>>(v123, {0, 1, 2});
+    Tensor<bool, 1, 1, 999> v126 =
+        emitc::tosa::greater_equal<Tensor<bool, 1, 1, 999>>(v125, v124);
+    Tensor<int32_t, 1, 1, 999> v127 =
+        emitc::tosa::cast<Tensor<int32_t, 1, 1, 999>>(v126);
+    Tensor<int32_t, 1, 1> v128 =
+        emitc::tosa::reduce_sum<Tensor<int32_t, 1, 1>,
+                                Tensor<int32_t, 1, 1, 999>>(v127, 2);
+    Tensor<int32_t, 1, 1, 1> v129 =
+        emitc::tosa::reshape<Tensor<int32_t, 1, 1, 1>>(v128);
+    Tensor<int32_t, 1, 1> v130 =
+        emitc::tosa::reshape<Tensor<int32_t, 1, 1>>(v129);
+    Tensor<float, 1, 1> v131 = emitc::tosa::cast<Tensor<float, 1, 1>>(v130);
+    Tensor<float, 1, 1> v132 = emitc::tosa::mul(v131, v40);
+    Tensor<float, 1, 1> v133 = emitc::tosa::mul(v132, v132);
+    Tensor<float, 1, 2> v134 =
+        emitc::tosa::concat<1, Tensor<float, 1, 2>>(v132, v133);
+    Tensor<int64_t, 1, 1, 1> v135 =
+        emitc::tosa::reshape<Tensor<int64_t, 1, 1, 1>>(v5);
+    Tensor<int64_t, 1, 1, 999> v136 =
+        emitc::broadcast_in_dim<Tensor<int64_t, 1, 1, 999>>(v135, {0, 1, 2});
+    Tensor<bool, 1, 1, 999> v137 =
+        emitc::tosa::greater_equal<Tensor<bool, 1, 1, 999>>(v136, v41);
+    Tensor<int32_t, 1, 1, 999> v138 =
+        emitc::tosa::cast<Tensor<int32_t, 1, 1, 999>>(v137);
+    Tensor<int32_t, 1, 1> v139 =
+        emitc::tosa::reduce_sum<Tensor<int32_t, 1, 1>,
+                                Tensor<int32_t, 1, 1, 999>>(v138, 2);
+    Tensor<int32_t, 1, 1, 1> v140 =
+        emitc::tosa::reshape<Tensor<int32_t, 1, 1, 1>>(v139);
+    Tensor<int32_t, 1, 1> v141 =
+        emitc::tosa::reshape<Tensor<int32_t, 1, 1>>(v140);
+    Tensor<float, 1, 1> v142 = emitc::tosa::cast<Tensor<float, 1, 1>>(v141);
+    Tensor<float, 1, 1> v143 = emitc::tosa::mul(v142, v40);
+    Tensor<float, 1, 1> v144 = emitc::tosa::mul(v143, v143);
+    Tensor<float, 1, 2> v145 =
+        emitc::tosa::concat<1, Tensor<float, 1, 2>>(v143, v144);
+    Tensor<int64_t, 999> v146 = emitc::tosa::cast<Tensor<int64_t, 999>>(v57);
+    Tensor<int64_t, 1, 1, 1> v147 =
+        emitc::tosa::reshape<Tensor<int64_t, 1, 1, 1>>(v24);
+    Tensor<int64_t, 1, 1, 999> v148 =
+        emitc::tosa::reshape<Tensor<int64_t, 1, 1, 999>>(v146);
+    Tensor<int64_t, 1, 1, 999> v149 =
+        emitc::broadcast_in_dim<Tensor<int64_t, 1, 1, 999>>(v147, {0, 1, 2});
+    Tensor<bool, 1, 1, 999> v150 =
+        emitc::tosa::greater_equal<Tensor<bool, 1, 1, 999>>(v149, v148);
+    Tensor<int32_t, 1, 1, 999> v151 =
+        emitc::tosa::cast<Tensor<int32_t, 1, 1, 999>>(v150);
+    Tensor<int32_t, 1, 1> v152 =
+        emitc::tosa::reduce_sum<Tensor<int32_t, 1, 1>,
+                                Tensor<int32_t, 1, 1, 999>>(v151, 2);
+    Tensor<int32_t, 1, 1, 1> v153 =
+        emitc::tosa::reshape<Tensor<int32_t, 1, 1, 1>>(v152);
+    Tensor<int32_t, 1, 1> v154 =
+        emitc::tosa::reshape<Tensor<int32_t, 1, 1>>(v153);
+    Tensor<float, 1, 1> v155 = emitc::tosa::cast<Tensor<float, 1, 1>>(v154);
+    Tensor<float, 1, 1> v156 = emitc::tosa::mul(v155, v40);
+    Tensor<float, 1, 1> v157 = emitc::tosa::mul(v156, v156);
+    Tensor<float, 1, 2> v158 =
+        emitc::tosa::concat<1, Tensor<float, 1, 2>>(v156, v157);
+    Tensor<int64_t, 999> v159 = emitc::tosa::cast<Tensor<int64_t, 999>>(v69);
+    Tensor<int64_t, 1, 1, 1> v160 =
+        emitc::tosa::reshape<Tensor<int64_t, 1, 1, 1>>(v35);
+    Tensor<int64_t, 1, 1, 999> v161 =
+        emitc::tosa::reshape<Tensor<int64_t, 1, 1, 999>>(v159);
+    Tensor<int64_t, 1, 1, 999> v162 =
+        emitc::broadcast_in_dim<Tensor<int64_t, 1, 1, 999>>(v160, {0, 1, 2});
+    Tensor<bool, 1, 1, 999> v163 =
+        emitc::tosa::greater_equal<Tensor<bool, 1, 1, 999>>(v162, v161);
+    Tensor<int32_t, 1, 1, 999> v164 =
+        emitc::tosa::cast<Tensor<int32_t, 1, 1, 999>>(v163);
+    Tensor<int32_t, 1, 1> v165 =
+        emitc::tosa::reduce_sum<Tensor<int32_t, 1, 1>,
+                                Tensor<int32_t, 1, 1, 999>>(v164, 2);
+    Tensor<int32_t, 1, 1, 1> v166 =
+        emitc::tosa::reshape<Tensor<int32_t, 1, 1, 1>>(v165);
+    Tensor<int32_t, 1, 1> v167 =
+        emitc::tosa::reshape<Tensor<int32_t, 1, 1>>(v166);
+    Tensor<float, 1, 1> v168 = emitc::tosa::cast<Tensor<float, 1, 1>>(v167);
+    Tensor<float, 1, 1> v169 = emitc::tosa::mul(v168, v40);
+    Tensor<float, 1, 1> v170 = emitc::tosa::mul(v169, v169);
+    Tensor<float, 1, 2> v171 =
+        emitc::tosa::concat<1, Tensor<float, 1, 2>>(v169, v170);
+    Tensor<int64_t, 999> v172 = emitc::tosa::cast<Tensor<int64_t, 999>>(v68);
+    Tensor<int64_t, 1, 1, 1> v173 =
+        emitc::tosa::reshape<Tensor<int64_t, 1, 1, 1>>(v16);
+    Tensor<int64_t, 1, 1, 999> v174 =
+        emitc::tosa::reshape<Tensor<int64_t, 1, 1, 999>>(v172);
+    Tensor<int64_t, 1, 1, 999> v175 =
+        emitc::broadcast_in_dim<Tensor<int64_t, 1, 1, 999>>(v173, {0, 1, 2});
+    Tensor<bool, 1, 1, 999> v176 =
+        emitc::tosa::greater_equal<Tensor<bool, 1, 1, 999>>(v175, v174);
+    Tensor<int32_t, 1, 1, 999> v177 =
+        emitc::tosa::cast<Tensor<int32_t, 1, 1, 999>>(v176);
+    Tensor<int32_t, 1, 1> v178 =
+        emitc::tosa::reduce_sum<Tensor<int32_t, 1, 1>,
+                                Tensor<int32_t, 1, 1, 999>>(v177, 2);
+    Tensor<int32_t, 1, 1, 1> v179 =
+        emitc::tosa::reshape<Tensor<int32_t, 1, 1, 1>>(v178);
+    Tensor<int32_t, 1, 1> v180 =
+        emitc::tosa::reshape<Tensor<int32_t, 1, 1>>(v179);
+    Tensor<float, 1, 1> v181 = emitc::tosa::cast<Tensor<float, 1, 1>>(v180);
+    Tensor<float, 1, 1> v182 = emitc::tosa::mul(v181, v40);
+    Tensor<float, 1, 1> v183 = emitc::tosa::mul(v182, v182);
+    Tensor<float, 1, 2> v184 =
+        emitc::tosa::concat<1, Tensor<float, 1, 2>>(v182, v183);
+    Tensor<int64_t, 999> v185 = emitc::tosa::cast<Tensor<int64_t, 999>>(v67);
+    Tensor<int64_t, 1, 1, 1> v186 =
+        emitc::tosa::reshape<Tensor<int64_t, 1, 1, 1>>(v23);
+    Tensor<int64_t, 1, 1, 999> v187 =
+        emitc::tosa::reshape<Tensor<int64_t, 1, 1, 999>>(v185);
+    Tensor<int64_t, 1, 1, 999> v188 =
+        emitc::broadcast_in_dim<Tensor<int64_t, 1, 1, 999>>(v186, {0, 1, 2});
+    Tensor<bool, 1, 1, 999> v189 =
+        emitc::tosa::greater_equal<Tensor<bool, 1, 1, 999>>(v188, v187);
+    Tensor<int32_t, 1, 1, 999> v190 =
+        emitc::tosa::cast<Tensor<int32_t, 1, 1, 999>>(v189);
+    Tensor<int32_t, 1, 1> v191 =
+        emitc::tosa::reduce_sum<Tensor<int32_t, 1, 1>,
+                                Tensor<int32_t, 1, 1, 999>>(v190, 2);
+    Tensor<int32_t, 1, 1, 1> v192 =
+        emitc::tosa::reshape<Tensor<int32_t, 1, 1, 1>>(v191);
+    Tensor<int32_t, 1, 1> v193 =
+        emitc::tosa::reshape<Tensor<int32_t, 1, 1>>(v192);
+    Tensor<float, 1, 1> v194 = emitc::tosa::cast<Tensor<float, 1, 1>>(v193);
+    Tensor<float, 1, 1> v195 = emitc::tosa::mul(v194, v40);
+    Tensor<float, 1, 1> v196 = emitc::tosa::mul(v195, v195);
+    Tensor<float, 1, 2> v197 =
+        emitc::tosa::concat<1, Tensor<float, 1, 2>>(v195, v196);
+    Tensor<int64_t, 999> v198 = emitc::tosa::cast<Tensor<int64_t, 999>>(v66);
+    Tensor<int64_t, 1, 1, 1> v199 =
+        emitc::tosa::reshape<Tensor<int64_t, 1, 1, 1>>(v29);
+    Tensor<int64_t, 1, 1, 999> v200 =
+        emitc::tosa::reshape<Tensor<int64_t, 1, 1, 999>>(v198);
+    Tensor<int64_t, 1, 1, 999> v201 =
+        emitc::broadcast_in_dim<Tensor<int64_t, 1, 1, 999>>(v199, {0, 1, 2});
+    Tensor<bool, 1, 1, 999> v202 =
+        emitc::tosa::greater_equal<Tensor<bool, 1, 1, 999>>(v201, v200);
+    Tensor<int32_t, 1, 1, 999> v203 =
+        emitc::tosa::cast<Tensor<int32_t, 1, 1, 999>>(v202);
+    Tensor<int32_t, 1, 1> v204 =
+        emitc::tosa::reduce_sum<Tensor<int32_t, 1, 1>,
+                                Tensor<int32_t, 1, 1, 999>>(v203, 2);
+    Tensor<int32_t, 1, 1, 1> v205 =
+        emitc::tosa::reshape<Tensor<int32_t, 1, 1, 1>>(v204);
+    Tensor<int32_t, 1, 1> v206 =
+        emitc::tosa::reshape<Tensor<int32_t, 1, 1>>(v205);
+    Tensor<float, 1, 1> v207 = emitc::tosa::cast<Tensor<float, 1, 1>>(v206);
+    Tensor<float, 1, 1> v208 = emitc::tosa::mul(v207, v40);
+    Tensor<float, 1, 1> v209 = emitc::tosa::mul(v208, v208);
+    Tensor<float, 1, 2> v210 =
+        emitc::tosa::concat<1, Tensor<float, 1, 2>>(v208, v209);
+    Tensor<int64_t, 1, 1, 1> v211 =
+        emitc::tosa::reshape<Tensor<int64_t, 1, 1, 1>>(v13);
+    Tensor<int64_t, 1, 1, 999> v212 =
+        emitc::broadcast_in_dim<Tensor<int64_t, 1, 1, 999>>(v211, {0, 1, 2});
+    Tensor<bool, 1, 1, 999> v213 =
+        emitc::tosa::greater_equal<Tensor<bool, 1, 1, 999>>(v212, v41);
+    Tensor<int32_t, 1, 1, 999> v214 =
+        emitc::tosa::cast<Tensor<int32_t, 1, 1, 999>>(v213);
+    Tensor<int32_t, 1, 1> v215 =
+        emitc::tosa::reduce_sum<Tensor<int32_t, 1, 1>,
+                                Tensor<int32_t, 1, 1, 999>>(v214, 2);
+    Tensor<int32_t, 1, 1, 1> v216 =
+        emitc::tosa::reshape<Tensor<int32_t, 1, 1, 1>>(v215);
+    Tensor<int32_t, 1, 1> v217 =
+        emitc::tosa::reshape<Tensor<int32_t, 1, 1>>(v216);
+    Tensor<float, 1, 1> v218 = emitc::tosa::cast<Tensor<float, 1, 1>>(v217);
+    Tensor<float, 1, 1> v219 = emitc::tosa::mul(v218, v40);
+    Tensor<float, 1, 1> v220 = emitc::tosa::mul(v219, v219);
+    Tensor<float, 1, 2> v221 =
+        emitc::tosa::concat<1, Tensor<float, 1, 2>>(v219, v220);
+    Tensor<int64_t, 999> v222 = emitc::tosa::cast<Tensor<int64_t, 999>>(v65);
+    Tensor<int64_t, 1, 1, 1> v223 =
+        emitc::tosa::reshape<Tensor<int64_t, 1, 1, 1>>(v2);
+    Tensor<int64_t, 1, 1, 999> v224 =
+        emitc::tosa::reshape<Tensor<int64_t, 1, 1, 999>>(v222);
+    Tensor<int64_t, 1, 1, 999> v225 =
+        emitc::broadcast_in_dim<Tensor<int64_t, 1, 1, 999>>(v223, {0, 1, 2});
+    Tensor<bool, 1, 1, 999> v226 =
+        emitc::tosa::greater_equal<Tensor<bool, 1, 1, 999>>(v225, v224);
+    Tensor<int32_t, 1, 1, 999> v227 =
+        emitc::tosa::cast<Tensor<int32_t, 1, 1, 999>>(v226);
+    Tensor<int32_t, 1, 1> v228 =
+        emitc::tosa::reduce_sum<Tensor<int32_t, 1, 1>,
+                                Tensor<int32_t, 1, 1, 999>>(v227, 2);
+    Tensor<int32_t, 1, 1, 1> v229 =
+        emitc::tosa::reshape<Tensor<int32_t, 1, 1, 1>>(v228);
+    Tensor<int32_t, 1, 1> v230 =
+        emitc::tosa::reshape<Tensor<int32_t, 1, 1>>(v229);
+    Tensor<float, 1, 1> v231 = emitc::tosa::cast<Tensor<float, 1, 1>>(v230);
+    Tensor<float, 1, 1> v232 = emitc::tosa::mul(v231, v40);
+    Tensor<float, 1, 1> v233 = emitc::tosa::mul(v232, v232);
+    Tensor<float, 1, 2> v234 =
+        emitc::tosa::concat<1, Tensor<float, 1, 2>>(v232, v233);
+    Tensor<int64_t, 999> v235 = emitc::tosa::cast<Tensor<int64_t, 999>>(v64);
+    Tensor<int64_t, 1, 1, 1> v236 =
+        emitc::tosa::reshape<Tensor<int64_t, 1, 1, 1>>(v11);
+    Tensor<int64_t, 1, 1, 999> v237 =
+        emitc::tosa::reshape<Tensor<int64_t, 1, 1, 999>>(v235);
+    Tensor<int64_t, 1, 1, 999> v238 =
+        emitc::broadcast_in_dim<Tensor<int64_t, 1, 1, 999>>(v236, {0, 1, 2});
+    Tensor<bool, 1, 1, 999> v239 =
+        emitc::tosa::greater_equal<Tensor<bool, 1, 1, 999>>(v238, v237);
+    Tensor<int32_t, 1, 1, 999> v240 =
+        emitc::tosa::cast<Tensor<int32_t, 1, 1, 999>>(v239);
+    Tensor<int32_t, 1, 1> v241 =
+        emitc::tosa::reduce_sum<Tensor<int32_t, 1, 1>,
+                                Tensor<int32_t, 1, 1, 999>>(v240, 2);
+    Tensor<int32_t, 1, 1, 1> v242 =
+        emitc::tosa::reshape<Tensor<int32_t, 1, 1, 1>>(v241);
+    Tensor<int32_t, 1, 1> v243 =
+        emitc::tosa::reshape<Tensor<int32_t, 1, 1>>(v242);
+    Tensor<float, 1, 1> v244 = emitc::tosa::cast<Tensor<float, 1, 1>>(v243);
+    Tensor<float, 1, 1> v245 = emitc::tosa::mul(v244, v40);
+    Tensor<float, 1, 1> v246 = emitc::tosa::mul(v245, v245);
+    Tensor<float, 1, 2> v247 =
+        emitc::tosa::concat<1, Tensor<float, 1, 2>>(v245, v246);
+    Tensor<int64_t, 1, 1, 1> v248 =
+        emitc::tosa::reshape<Tensor<int64_t, 1, 1, 1>>(v32);
+    Tensor<int64_t, 1, 1, 999> v249 =
+        emitc::broadcast_in_dim<Tensor<int64_t, 1, 1, 999>>(v248, {0, 1, 2});
+    Tensor<bool, 1, 1, 999> v250 =
+        emitc::tosa::greater_equal<Tensor<bool, 1, 1, 999>>(v249, v41);
+    Tensor<int32_t, 1, 1, 999> v251 =
+        emitc::tosa::cast<Tensor<int32_t, 1, 1, 999>>(v250);
+    Tensor<int32_t, 1, 1> v252 =
+        emitc::tosa::reduce_sum<Tensor<int32_t, 1, 1>,
+                                Tensor<int32_t, 1, 1, 999>>(v251, 2);
+    Tensor<int32_t, 1, 1, 1> v253 =
+        emitc::tosa::reshape<Tensor<int32_t, 1, 1, 1>>(v252);
+    Tensor<int32_t, 1, 1> v254 =
+        emitc::tosa::reshape<Tensor<int32_t, 1, 1>>(v253);
+    Tensor<float, 1, 1> v255 = emitc::tosa::cast<Tensor<float, 1, 1>>(v254);
+    Tensor<float, 1, 1> v256 = emitc::tosa::mul(v255, v40);
+    Tensor<float, 1, 1> v257 = emitc::tosa::mul(v256, v256);
+    Tensor<float, 1, 2> v258 =
+        emitc::tosa::concat<1, Tensor<float, 1, 2>>(v256, v257);
+    Tensor<int64_t, 999> v259 = emitc::tosa::cast<Tensor<int64_t, 999>>(v63);
+    Tensor<int64_t, 1, 1, 1> v260 =
+        emitc::tosa::reshape<Tensor<int64_t, 1, 1, 1>>(v14);
+    Tensor<int64_t, 1, 1, 999> v261 =
+        emitc::tosa::reshape<Tensor<int64_t, 1, 1, 999>>(v259);
+    Tensor<int64_t, 1, 1, 999> v262 =
+        emitc::broadcast_in_dim<Tensor<int64_t, 1, 1, 999>>(v260, {0, 1, 2});
+    Tensor<bool, 1, 1, 999> v263 =
+        emitc::tosa::greater_equal<Tensor<bool, 1, 1, 999>>(v262, v261);
+    Tensor<int32_t, 1, 1, 999> v264 =
+        emitc::tosa::cast<Tensor<int32_t, 1, 1, 999>>(v263);
+    Tensor<int32_t, 1, 1> v265 =
+        emitc::tosa::reduce_sum<Tensor<int32_t, 1, 1>,
+                                Tensor<int32_t, 1, 1, 999>>(v264, 2);
+    Tensor<int32_t, 1, 1, 1> v266 =
+        emitc::tosa::reshape<Tensor<int32_t, 1, 1, 1>>(v265);
+    Tensor<int32_t, 1, 1> v267 =
+        emitc::tosa::reshape<Tensor<int32_t, 1, 1>>(v266);
+    Tensor<float, 1, 1> v268 = emitc::tosa::cast<Tensor<float, 1, 1>>(v267);
+    Tensor<float, 1, 1> v269 = emitc::tosa::mul(v268, v40);
+    Tensor<float, 1, 1> v270 = emitc::tosa::mul(v269, v269);
+    Tensor<float, 1, 2> v271 =
+        emitc::tosa::concat<1, Tensor<float, 1, 2>>(v269, v270);
+    Tensor<int64_t, 1, 1, 1> v272 =
+        emitc::tosa::reshape<Tensor<int64_t, 1, 1, 1>>(v10);
+    Tensor<int64_t, 1, 1, 999> v273 =
+        emitc::broadcast_in_dim<Tensor<int64_t, 1, 1, 999>>(v272, {0, 1, 2});
+    Tensor<bool, 1, 1, 999> v274 =
+        emitc::tosa::greater_equal<Tensor<bool, 1, 1, 999>>(v273, v41);
+    Tensor<int32_t, 1, 1, 999> v275 =
+        emitc::tosa::cast<Tensor<int32_t, 1, 1, 999>>(v274);
+    Tensor<int32_t, 1, 1> v276 =
+        emitc::tosa::reduce_sum<Tensor<int32_t, 1, 1>,
+                                Tensor<int32_t, 1, 1, 999>>(v275, 2);
+    Tensor<int32_t, 1, 1, 1> v277 =
+        emitc::tosa::reshape<Tensor<int32_t, 1, 1, 1>>(v276);
+    Tensor<int32_t, 1, 1> v278 =
+        emitc::tosa::reshape<Tensor<int32_t, 1, 1>>(v277);
+    Tensor<float, 1, 1> v279 = emitc::tosa::cast<Tensor<float, 1, 1>>(v278);
+    Tensor<float, 1, 1> v280 = emitc::tosa::mul(v279, v40);
+    Tensor<float, 1, 1> v281 = emitc::tosa::mul(v280, v280);
+    Tensor<float, 1, 2> v282 =
+        emitc::tosa::concat<1, Tensor<float, 1, 2>>(v280, v281);
+    Tensor<int64_t, 999> v283 = emitc::tosa::cast<Tensor<int64_t, 999>>(v62);
+    Tensor<int64_t, 1, 1, 1> v284 =
+        emitc::tosa::reshape<Tensor<int64_t, 1, 1, 1>>(v21);
+    Tensor<int64_t, 1, 1, 999> v285 =
+        emitc::tosa::reshape<Tensor<int64_t, 1, 1, 999>>(v283);
+    Tensor<int64_t, 1, 1, 999> v286 =
+        emitc::broadcast_in_dim<Tensor<int64_t, 1, 1, 999>>(v284, {0, 1, 2});
+    Tensor<bool, 1, 1, 999> v287 =
+        emitc::tosa::greater_equal<Tensor<bool, 1, 1, 999>>(v286, v285);
+    Tensor<int32_t, 1, 1, 999> v288 =
+        emitc::tosa::cast<Tensor<int32_t, 1, 1, 999>>(v287);
+    Tensor<int32_t, 1, 1> v289 =
+        emitc::tosa::reduce_sum<Tensor<int32_t, 1, 1>,
+                                Tensor<int32_t, 1, 1, 999>>(v288, 2);
+    Tensor<int32_t, 1, 1, 1> v290 =
+        emitc::tosa::reshape<Tensor<int32_t, 1, 1, 1>>(v289);
+    Tensor<int32_t, 1, 1> v291 =
+        emitc::tosa::reshape<Tensor<int32_t, 1, 1>>(v290);
+    Tensor<float, 1, 1> v292 = emitc::tosa::cast<Tensor<float, 1, 1>>(v291);
+    Tensor<float, 1, 1> v293 = emitc::tosa::mul(v292, v40);
+    Tensor<float, 1, 1> v294 = emitc::tosa::mul(v293, v293);
+    Tensor<float, 1, 2> v295 =
+        emitc::tosa::concat<1, Tensor<float, 1, 2>>(v293, v294);
+    Tensor<int64_t, 9999> v296 = emitc::tosa::cast<Tensor<int64_t, 9999>>(v61);
+    Tensor<int64_t, 1, 1, 1> v297 =
+        emitc::tosa::reshape<Tensor<int64_t, 1, 1, 1>>(v33);
+    Tensor<int64_t, 1, 1, 9999> v298 =
+        emitc::tosa::reshape<Tensor<int64_t, 1, 1, 9999>>(v296);
+    Tensor<int64_t, 1, 1, 9999> v299 =
+        emitc::broadcast_in_dim<Tensor<int64_t, 1, 1, 9999>>(v297, {0, 1, 2});
+    Tensor<bool, 1, 1, 9999> v300 =
+        emitc::tosa::greater_equal<Tensor<bool, 1, 1, 9999>>(v299, v298);
+    Tensor<int32_t, 1, 1, 9999> v301 =
+        emitc::tosa::cast<Tensor<int32_t, 1, 1, 9999>>(v300);
+    Tensor<int32_t, 1, 1> v302 =
+        emitc::tosa::reduce_sum<Tensor<int32_t, 1, 1>,
+                                Tensor<int32_t, 1, 1, 9999>>(v301, 2);
+    Tensor<int32_t, 1, 1, 1> v303 =
+        emitc::tosa::reshape<Tensor<int32_t, 1, 1, 1>>(v302);
+    Tensor<int32_t, 1, 1> v304 =
+        emitc::tosa::reshape<Tensor<int32_t, 1, 1>>(v303);
+    Tensor<float, 1, 1> v305 = emitc::tosa::cast<Tensor<float, 1, 1>>(v304);
+    Tensor<float, 1, 1> v306 = emitc::tosa::mul(v305, v42);
+    Tensor<float, 1, 1> v307 = emitc::tosa::mul(v306, v306);
+    Tensor<float, 1, 2> v308 =
+        emitc::tosa::concat<1, Tensor<float, 1, 2>>(v306, v307);
+    Tensor<int64_t, 9999> v309 = emitc::tosa::cast<Tensor<int64_t, 9999>>(v60);
+    Tensor<int64_t, 1, 1, 1> v310 =
+        emitc::tosa::reshape<Tensor<int64_t, 1, 1, 1>>(v30);
+    Tensor<int64_t, 1, 1, 9999> v311 =
+        emitc::tosa::reshape<Tensor<int64_t, 1, 1, 9999>>(v309);
+    Tensor<int64_t, 1, 1, 9999> v312 =
+        emitc::broadcast_in_dim<Tensor<int64_t, 1, 1, 9999>>(v310, {0, 1, 2});
+    Tensor<bool, 1, 1, 9999> v313 =
+        emitc::tosa::greater_equal<Tensor<bool, 1, 1, 9999>>(v312, v311);
+    Tensor<int32_t, 1, 1, 9999> v314 =
+        emitc::tosa::cast<Tensor<int32_t, 1, 1, 9999>>(v313);
+    Tensor<int32_t, 1, 1> v315 =
+        emitc::tosa::reduce_sum<Tensor<int32_t, 1, 1>,
+                                Tensor<int32_t, 1, 1, 9999>>(v314, 2);
+    Tensor<int32_t, 1, 1, 1> v316 =
+        emitc::tosa::reshape<Tensor<int32_t, 1, 1, 1>>(v315);
+    Tensor<int32_t, 1, 1> v317 =
+        emitc::tosa::reshape<Tensor<int32_t, 1, 1>>(v316);
+    Tensor<float, 1, 1> v318 = emitc::tosa::cast<Tensor<float, 1, 1>>(v317);
+    Tensor<float, 1, 1> v319 = emitc::tosa::mul(v318, v42);
+    Tensor<float, 1, 1> v320 = emitc::tosa::mul(v319, v319);
+    Tensor<float, 1, 2> v321 =
+        emitc::tosa::concat<1, Tensor<float, 1, 2>>(v319, v320);
+    Tensor<int64_t, 999> v322 = emitc::tosa::cast<Tensor<int64_t, 999>>(v59);
+    Tensor<int64_t, 1, 1, 1> v323 =
+        emitc::tosa::reshape<Tensor<int64_t, 1, 1, 1>>(v19);
+    Tensor<int64_t, 1, 1, 999> v324 =
+        emitc::tosa::reshape<Tensor<int64_t, 1, 1, 999>>(v322);
+    Tensor<int64_t, 1, 1, 999> v325 =
+        emitc::broadcast_in_dim<Tensor<int64_t, 1, 1, 999>>(v323, {0, 1, 2});
+    Tensor<bool, 1, 1, 999> v326 =
+        emitc::tosa::greater_equal<Tensor<bool, 1, 1, 999>>(v325, v324);
+    Tensor<int32_t, 1, 1, 999> v327 =
+        emitc::tosa::cast<Tensor<int32_t, 1, 1, 999>>(v326);
+    Tensor<int32_t, 1, 1> v328 =
+        emitc::tosa::reduce_sum<Tensor<int32_t, 1, 1>,
+                                Tensor<int32_t, 1, 1, 999>>(v327, 2);
+    Tensor<int32_t, 1, 1, 1> v329 =
+        emitc::tosa::reshape<Tensor<int32_t, 1, 1, 1>>(v328);
+    Tensor<int32_t, 1, 1> v330 =
+        emitc::tosa::reshape<Tensor<int32_t, 1, 1>>(v329);
+    Tensor<float, 1, 1> v331 = emitc::tosa::cast<Tensor<float, 1, 1>>(v330);
+    Tensor<float, 1, 1> v332 = emitc::tosa::mul(v331, v40);
+    Tensor<float, 1, 1> v333 = emitc::tosa::mul(v332, v332);
+    Tensor<float, 1, 2> v334 =
+        emitc::tosa::concat<1, Tensor<float, 1, 2>>(v332, v333);
+    Tensor<int64_t, 999> v335 = emitc::tosa::cast<Tensor<int64_t, 999>>(v58);
+    Tensor<int64_t, 1, 1, 1> v336 =
+        emitc::tosa::reshape<Tensor<int64_t, 1, 1, 1>>(v18);
+    Tensor<int64_t, 1, 1, 999> v337 =
+        emitc::tosa::reshape<Tensor<int64_t, 1, 1, 999>>(v335);
+    Tensor<int64_t, 1, 1, 999> v338 =
+        emitc::broadcast_in_dim<Tensor<int64_t, 1, 1, 999>>(v336, {0, 1, 2});
+    Tensor<bool, 1, 1, 999> v339 =
+        emitc::tosa::greater_equal<Tensor<bool, 1, 1, 999>>(v338, v337);
+    Tensor<int32_t, 1, 1, 999> v340 =
+        emitc::tosa::cast<Tensor<int32_t, 1, 1, 999>>(v339);
+    Tensor<int32_t, 1, 1> v341 =
+        emitc::tosa::reduce_sum<Tensor<int32_t, 1, 1>,
+                                Tensor<int32_t, 1, 1, 999>>(v340, 2);
+    Tensor<int32_t, 1, 1, 1> v342 =
+        emitc::tosa::reshape<Tensor<int32_t, 1, 1, 1>>(v341);
+    Tensor<int32_t, 1, 1> v343 =
+        emitc::tosa::reshape<Tensor<int32_t, 1, 1>>(v342);
+    Tensor<float, 1, 1> v344 = emitc::tosa::cast<Tensor<float, 1, 1>>(v343);
+    Tensor<float, 1, 1> v345 = emitc::tosa::mul(v344, v40);
+    Tensor<float, 1, 1> v346 = emitc::tosa::mul(v345, v345);
+    Tensor<float, 1, 2> v347 =
+        emitc::tosa::concat<1, Tensor<float, 1, 2>>(v345, v346);
+    Tensor<int64_t, 999> v348 = emitc::tosa::cast<Tensor<int64_t, 999>>(v57);
+    Tensor<int64_t, 1, 1, 1> v349 =
+        emitc::tosa::reshape<Tensor<int64_t, 1, 1, 1>>(v9);
+    Tensor<int64_t, 1, 1, 999> v350 =
+        emitc::tosa::reshape<Tensor<int64_t, 1, 1, 999>>(v348);
+    Tensor<int64_t, 1, 1, 999> v351 =
+        emitc::broadcast_in_dim<Tensor<int64_t, 1, 1, 999>>(v349, {0, 1, 2});
+    Tensor<bool, 1, 1, 999> v352 =
+        emitc::tosa::greater_equal<Tensor<bool, 1, 1, 999>>(v351, v350);
+    Tensor<int32_t, 1, 1, 999> v353 =
+        emitc::tosa::cast<Tensor<int32_t, 1, 1, 999>>(v352);
+    Tensor<int32_t, 1, 1> v354 =
+        emitc::tosa::reduce_sum<Tensor<int32_t, 1, 1>,
+                                Tensor<int32_t, 1, 1, 999>>(v353, 2);
+    Tensor<int32_t, 1, 1, 1> v355 =
+        emitc::tosa::reshape<Tensor<int32_t, 1, 1, 1>>(v354);
+    Tensor<int32_t, 1, 1> v356 =
+        emitc::tosa::reshape<Tensor<int32_t, 1, 1>>(v355);
+    Tensor<float, 1, 1> v357 = emitc::tosa::cast<Tensor<float, 1, 1>>(v356);
+    Tensor<float, 1, 1> v358 = emitc::tosa::mul(v357, v40);
+    Tensor<float, 1, 1> v359 = emitc::tosa::mul(v358, v358);
+    Tensor<float, 1, 2> v360 =
+        emitc::tosa::concat<1, Tensor<float, 1, 2>>(v358, v359);
+    Tensor<int64_t, 999> v361 = emitc::tosa::cast<Tensor<int64_t, 999>>(v56);
+    Tensor<int64_t, 1, 1, 1> v362 =
+        emitc::tosa::reshape<Tensor<int64_t, 1, 1, 1>>(v27);
+    Tensor<int64_t, 1, 1, 999> v363 =
+        emitc::tosa::reshape<Tensor<int64_t, 1, 1, 999>>(v361);
+    Tensor<int64_t, 1, 1, 999> v364 =
+        emitc::broadcast_in_dim<Tensor<int64_t, 1, 1, 999>>(v362, {0, 1, 2});
+    Tensor<bool, 1, 1, 999> v365 =
+        emitc::tosa::greater_equal<Tensor<bool, 1, 1, 999>>(v364, v363);
+    Tensor<int32_t, 1, 1, 999> v366 =
+        emitc::tosa::cast<Tensor<int32_t, 1, 1, 999>>(v365);
+    Tensor<int32_t, 1, 1> v367 =
+        emitc::tosa::reduce_sum<Tensor<int32_t, 1, 1>,
+                                Tensor<int32_t, 1, 1, 999>>(v366, 2);
+    Tensor<int32_t, 1, 1, 1> v368 =
+        emitc::tosa::reshape<Tensor<int32_t, 1, 1, 1>>(v367);
+    Tensor<int32_t, 1, 1> v369 =
+        emitc::tosa::reshape<Tensor<int32_t, 1, 1>>(v368);
+    Tensor<float, 1, 1> v370 = emitc::tosa::cast<Tensor<float, 1, 1>>(v369);
+    Tensor<float, 1, 1> v371 = emitc::tosa::mul(v370, v40);
+    Tensor<float, 1, 1> v372 = emitc::tosa::mul(v371, v371);
+    Tensor<float, 1, 2> v373 =
+        emitc::tosa::concat<1, Tensor<float, 1, 2>>(v371, v372);
+    Tensor<int64_t, 999> v374 = emitc::tosa::cast<Tensor<int64_t, 999>>(v55);
+    Tensor<int64_t, 1, 1, 1> v375 =
+        emitc::tosa::reshape<Tensor<int64_t, 1, 1, 1>>(v26);
+    Tensor<int64_t, 1, 1, 999> v376 =
+        emitc::tosa::reshape<Tensor<int64_t, 1, 1, 999>>(v374);
+    Tensor<int64_t, 1, 1, 999> v377 =
+        emitc::broadcast_in_dim<Tensor<int64_t, 1, 1, 999>>(v375, {0, 1, 2});
+    Tensor<bool, 1, 1, 999> v378 =
+        emitc::tosa::greater_equal<Tensor<bool, 1, 1, 999>>(v377, v376);
+    Tensor<int32_t, 1, 1, 999> v379 =
+        emitc::tosa::cast<Tensor<int32_t, 1, 1, 999>>(v378);
+    Tensor<int32_t, 1, 1> v380 =
+        emitc::tosa::reduce_sum<Tensor<int32_t, 1, 1>,
+                                Tensor<int32_t, 1, 1, 999>>(v379, 2);
+    Tensor<int32_t, 1, 1, 1> v381 =
+        emitc::tosa::reshape<Tensor<int32_t, 1, 1, 1>>(v380);
+    Tensor<int32_t, 1, 1> v382 =
+        emitc::tosa::reshape<Tensor<int32_t, 1, 1>>(v381);
+    Tensor<float, 1, 1> v383 = emitc::tosa::cast<Tensor<float, 1, 1>>(v382);
+    Tensor<float, 1, 1> v384 = emitc::tosa::mul(v383, v40);
+    Tensor<float, 1, 1> v385 = emitc::tosa::mul(v384, v384);
+    Tensor<float, 1, 2> v386 =
+        emitc::tosa::concat<1, Tensor<float, 1, 2>>(v384, v385);
+    Tensor<int64_t, 999> v387 = emitc::tosa::cast<Tensor<int64_t, 999>>(v54);
+    Tensor<int64_t, 1, 1, 1> v388 =
+        emitc::tosa::reshape<Tensor<int64_t, 1, 1, 1>>(v25);
+    Tensor<int64_t, 1, 1, 999> v389 =
+        emitc::tosa::reshape<Tensor<int64_t, 1, 1, 999>>(v387);
+    Tensor<int64_t, 1, 1, 999> v390 =
+        emitc::broadcast_in_dim<Tensor<int64_t, 1, 1, 999>>(v388, {0, 1, 2});
+    Tensor<bool, 1, 1, 999> v391 =
+        emitc::tosa::greater_equal<Tensor<bool, 1, 1, 999>>(v390, v389);
+    Tensor<int32_t, 1, 1, 999> v392 =
+        emitc::tosa::cast<Tensor<int32_t, 1, 1, 999>>(v391);
+    Tensor<int32_t, 1, 1> v393 =
+        emitc::tosa::reduce_sum<Tensor<int32_t, 1, 1>,
+                                Tensor<int32_t, 1, 1, 999>>(v392, 2);
+    Tensor<int32_t, 1, 1, 1> v394 =
+        emitc::tosa::reshape<Tensor<int32_t, 1, 1, 1>>(v393);
+    Tensor<int32_t, 1, 1> v395 =
+        emitc::tosa::reshape<Tensor<int32_t, 1, 1>>(v394);
+    Tensor<float, 1, 1> v396 = emitc::tosa::cast<Tensor<float, 1, 1>>(v395);
+    Tensor<float, 1, 1> v397 = emitc::tosa::mul(v396, v40);
+    Tensor<float, 1, 1> v398 = emitc::tosa::mul(v397, v397);
+    Tensor<float, 1, 2> v399 =
+        emitc::tosa::concat<1, Tensor<float, 1, 2>>(v397, v398);
+    Tensor<int64_t, 999> v400 = emitc::tosa::cast<Tensor<int64_t, 999>>(v53);
+    Tensor<int64_t, 1, 1, 1> v401 =
+        emitc::tosa::reshape<Tensor<int64_t, 1, 1, 1>>(v20);
+    Tensor<int64_t, 1, 1, 999> v402 =
+        emitc::tosa::reshape<Tensor<int64_t, 1, 1, 999>>(v400);
+    Tensor<int64_t, 1, 1, 999> v403 =
+        emitc::broadcast_in_dim<Tensor<int64_t, 1, 1, 999>>(v401, {0, 1, 2});
+    Tensor<bool, 1, 1, 999> v404 =
+        emitc::tosa::greater_equal<Tensor<bool, 1, 1, 999>>(v403, v402);
+    Tensor<int32_t, 1, 1, 999> v405 =
+        emitc::tosa::cast<Tensor<int32_t, 1, 1, 999>>(v404);
+    Tensor<int32_t, 1, 1> v406 =
+        emitc::tosa::reduce_sum<Tensor<int32_t, 1, 1>,
+                                Tensor<int32_t, 1, 1, 999>>(v405, 2);
+    Tensor<int32_t, 1, 1, 1> v407 =
+        emitc::tosa::reshape<Tensor<int32_t, 1, 1, 1>>(v406);
+    Tensor<int32_t, 1, 1> v408 =
+        emitc::tosa::reshape<Tensor<int32_t, 1, 1>>(v407);
+    Tensor<float, 1, 1> v409 = emitc::tosa::cast<Tensor<float, 1, 1>>(v408);
+    Tensor<float, 1, 1> v410 = emitc::tosa::mul(v409, v40);
+    Tensor<float, 1, 1> v411 = emitc::tosa::mul(v410, v410);
+    Tensor<float, 1, 2> v412 =
+        emitc::tosa::concat<1, Tensor<float, 1, 2>>(v410, v411);
+    Tensor<int64_t, 1, 1, 1> v413 =
+        emitc::tosa::reshape<Tensor<int64_t, 1, 1, 1>>(v36);
+    Tensor<int64_t, 1, 1, 999> v414 =
+        emitc::broadcast_in_dim<Tensor<int64_t, 1, 1, 999>>(v413, {0, 1, 2});
+    Tensor<bool, 1, 1, 999> v415 =
+        emitc::tosa::greater_equal<Tensor<bool, 1, 1, 999>>(v414, v41);
+    Tensor<int32_t, 1, 1, 999> v416 =
+        emitc::tosa::cast<Tensor<int32_t, 1, 1, 999>>(v415);
+    Tensor<int32_t, 1, 1> v417 =
+        emitc::tosa::reduce_sum<Tensor<int32_t, 1, 1>,
+                                Tensor<int32_t, 1, 1, 999>>(v416, 2);
+    Tensor<int32_t, 1, 1, 1> v418 =
+        emitc::tosa::reshape<Tensor<int32_t, 1, 1, 1>>(v417);
+    Tensor<int32_t, 1, 1> v419 =
+        emitc::tosa::reshape<Tensor<int32_t, 1, 1>>(v418);
+    Tensor<float, 1, 1> v420 = emitc::tosa::cast<Tensor<float, 1, 1>>(v419);
+    Tensor<float, 1, 1> v421 = emitc::tosa::mul(v420, v40);
+    Tensor<float, 1, 1> v422 = emitc::tosa::mul(v421, v421);
+    Tensor<float, 1, 2> v423 =
+        emitc::tosa::concat<1, Tensor<float, 1, 2>>(v421, v422);
+    Tensor<int64_t, 999> v424 = emitc::tosa::cast<Tensor<int64_t, 999>>(v52);
+    Tensor<int64_t, 1, 1, 1> v425 =
+        emitc::tosa::reshape<Tensor<int64_t, 1, 1, 1>>(v22);
+    Tensor<int64_t, 1, 1, 999> v426 =
+        emitc::tosa::reshape<Tensor<int64_t, 1, 1, 999>>(v424);
+    Tensor<int64_t, 1, 1, 999> v427 =
+        emitc::broadcast_in_dim<Tensor<int64_t, 1, 1, 999>>(v425, {0, 1, 2});
+    Tensor<bool, 1, 1, 999> v428 =
+        emitc::tosa::greater_equal<Tensor<bool, 1, 1, 999>>(v427, v426);
+    Tensor<int32_t, 1, 1, 999> v429 =
+        emitc::tosa::cast<Tensor<int32_t, 1, 1, 999>>(v428);
+    Tensor<int32_t, 1, 1> v430 =
+        emitc::tosa::reduce_sum<Tensor<int32_t, 1, 1>,
+                                Tensor<int32_t, 1, 1, 999>>(v429, 2);
+    Tensor<int32_t, 1, 1, 1> v431 =
+        emitc::tosa::reshape<Tensor<int32_t, 1, 1, 1>>(v430);
+    Tensor<int32_t, 1, 1> v432 =
+        emitc::tosa::reshape<Tensor<int32_t, 1, 1>>(v431);
+    Tensor<float, 1, 1> v433 = emitc::tosa::cast<Tensor<float, 1, 1>>(v432);
+    Tensor<float, 1, 1> v434 = emitc::tosa::mul(v433, v40);
+    Tensor<float, 1, 1> v435 = emitc::tosa::mul(v434, v434);
+    Tensor<float, 1, 2> v436 =
+        emitc::tosa::concat<1, Tensor<float, 1, 2>>(v434, v435);
+    Tensor<int64_t, 999> v437 = emitc::tosa::cast<Tensor<int64_t, 999>>(v51);
+    Tensor<int64_t, 1, 1, 1> v438 =
+        emitc::tosa::reshape<Tensor<int64_t, 1, 1, 1>>(v12);
+    Tensor<int64_t, 1, 1, 999> v439 =
+        emitc::tosa::reshape<Tensor<int64_t, 1, 1, 999>>(v437);
+    Tensor<int64_t, 1, 1, 999> v440 =
+        emitc::broadcast_in_dim<Tensor<int64_t, 1, 1, 999>>(v438, {0, 1, 2});
+    Tensor<bool, 1, 1, 999> v441 =
+        emitc::tosa::greater_equal<Tensor<bool, 1, 1, 999>>(v440, v439);
+    Tensor<int32_t, 1, 1, 999> v442 =
+        emitc::tosa::cast<Tensor<int32_t, 1, 1, 999>>(v441);
+    Tensor<int32_t, 1, 1> v443 =
+        emitc::tosa::reduce_sum<Tensor<int32_t, 1, 1>,
+                                Tensor<int32_t, 1, 1, 999>>(v442, 2);
+    Tensor<int32_t, 1, 1, 1> v444 =
+        emitc::tosa::reshape<Tensor<int32_t, 1, 1, 1>>(v443);
+    Tensor<int32_t, 1, 1> v445 =
+        emitc::tosa::reshape<Tensor<int32_t, 1, 1>>(v444);
+    Tensor<float, 1, 1> v446 = emitc::tosa::cast<Tensor<float, 1, 1>>(v445);
+    Tensor<float, 1, 1> v447 = emitc::tosa::mul(v446, v40);
+    Tensor<float, 1, 1> v448 = emitc::tosa::mul(v447, v447);
+    Tensor<float, 1, 2> v449 =
+        emitc::tosa::concat<1, Tensor<float, 1, 2>>(v447, v448);
+    Tensor<int64_t, 999> v450 = emitc::tosa::cast<Tensor<int64_t, 999>>(v50);
+    Tensor<int64_t, 1, 1, 1> v451 =
+        emitc::tosa::reshape<Tensor<int64_t, 1, 1, 1>>(v15);
+    Tensor<int64_t, 1, 1, 999> v452 =
+        emitc::tosa::reshape<Tensor<int64_t, 1, 1, 999>>(v450);
+    Tensor<int64_t, 1, 1, 999> v453 =
+        emitc::broadcast_in_dim<Tensor<int64_t, 1, 1, 999>>(v451, {0, 1, 2});
+    Tensor<bool, 1, 1, 999> v454 =
+        emitc::tosa::greater_equal<Tensor<bool, 1, 1, 999>>(v453, v452);
+    Tensor<int32_t, 1, 1, 999> v455 =
+        emitc::tosa::cast<Tensor<int32_t, 1, 1, 999>>(v454);
+    Tensor<int32_t, 1, 1> v456 =
+        emitc::tosa::reduce_sum<Tensor<int32_t, 1, 1>,
+                                Tensor<int32_t, 1, 1, 999>>(v455, 2);
+    Tensor<int32_t, 1, 1, 1> v457 =
+        emitc::tosa::reshape<Tensor<int32_t, 1, 1, 1>>(v456);
+    Tensor<int32_t, 1, 1> v458 =
+        emitc::tosa::reshape<Tensor<int32_t, 1, 1>>(v457);
+    Tensor<float, 1, 1> v459 = emitc::tosa::cast<Tensor<float, 1, 1>>(v458);
+    Tensor<float, 1, 1> v460 = emitc::tosa::mul(v459, v40);
+    Tensor<float, 1, 1> v461 = emitc::tosa::mul(v460, v460);
+    Tensor<float, 1, 2> v462 =
+        emitc::tosa::concat<1, Tensor<float, 1, 2>>(v460, v461);
+    Tensor<int64_t, 999> v463 = emitc::tosa::cast<Tensor<int64_t, 999>>(v49);
+    Tensor<int64_t, 1, 1, 1> v464 =
+        emitc::tosa::reshape<Tensor<int64_t, 1, 1, 1>>(v8);
+    Tensor<int64_t, 1, 1, 999> v465 =
+        emitc::tosa::reshape<Tensor<int64_t, 1, 1, 999>>(v463);
+    Tensor<int64_t, 1, 1, 999> v466 =
+        emitc::broadcast_in_dim<Tensor<int64_t, 1, 1, 999>>(v464, {0, 1, 2});
+    Tensor<bool, 1, 1, 999> v467 =
+        emitc::tosa::greater_equal<Tensor<bool, 1, 1, 999>>(v466, v465);
+    Tensor<int32_t, 1, 1, 999> v468 =
+        emitc::tosa::cast<Tensor<int32_t, 1, 1, 999>>(v467);
+    Tensor<int32_t, 1, 1> v469 =
+        emitc::tosa::reduce_sum<Tensor<int32_t, 1, 1>,
+                                Tensor<int32_t, 1, 1, 999>>(v468, 2);
+    Tensor<int32_t, 1, 1, 1> v470 =
+        emitc::tosa::reshape<Tensor<int32_t, 1, 1, 1>>(v469);
+    Tensor<int32_t, 1, 1> v471 =
+        emitc::tosa::reshape<Tensor<int32_t, 1, 1>>(v470);
+    Tensor<float, 1, 1> v472 = emitc::tosa::cast<Tensor<float, 1, 1>>(v471);
+    Tensor<float, 1, 1> v473 = emitc::tosa::mul(v472, v40);
+    Tensor<float, 1, 1> v474 = emitc::tosa::mul(v473, v473);
+    Tensor<float, 1, 2> v475 =
+        emitc::tosa::concat<1, Tensor<float, 1, 2>>(v473, v474);
+    Tensor<int64_t, 999> v476 = emitc::tosa::cast<Tensor<int64_t, 999>>(v48);
+    Tensor<int64_t, 1, 1, 1> v477 =
+        emitc::tosa::reshape<Tensor<int64_t, 1, 1, 1>>(v6);
+    Tensor<int64_t, 1, 1, 999> v478 =
+        emitc::tosa::reshape<Tensor<int64_t, 1, 1, 999>>(v476);
+    Tensor<int64_t, 1, 1, 999> v479 =
+        emitc::broadcast_in_dim<Tensor<int64_t, 1, 1, 999>>(v477, {0, 1, 2});
+    Tensor<bool, 1, 1, 999> v480 =
+        emitc::tosa::greater_equal<Tensor<bool, 1, 1, 999>>(v479, v478);
+    Tensor<int32_t, 1, 1, 999> v481 =
+        emitc::tosa::cast<Tensor<int32_t, 1, 1, 999>>(v480);
+    Tensor<int32_t, 1, 1> v482 =
+        emitc::tosa::reduce_sum<Tensor<int32_t, 1, 1>,
+                                Tensor<int32_t, 1, 1, 999>>(v481, 2);
+    Tensor<int32_t, 1, 1, 1> v483 =
+        emitc::tosa::reshape<Tensor<int32_t, 1, 1, 1>>(v482);
+    Tensor<int32_t, 1, 1> v484 =
+        emitc::tosa::reshape<Tensor<int32_t, 1, 1>>(v483);
+    Tensor<float, 1, 1> v485 = emitc::tosa::cast<Tensor<float, 1, 1>>(v484);
+    Tensor<float, 1, 1> v486 = emitc::tosa::mul(v485, v40);
+    Tensor<float, 1, 1> v487 = emitc::tosa::mul(v486, v486);
+    Tensor<float, 1, 2> v488 =
+        emitc::tosa::concat<1, Tensor<float, 1, 2>>(v486, v487);
+    Tensor<int64_t, 999> v489 = emitc::tosa::cast<Tensor<int64_t, 999>>(v47);
+    Tensor<int64_t, 1, 1, 1> v490 =
+        emitc::tosa::reshape<Tensor<int64_t, 1, 1, 1>>(v7);
+    Tensor<int64_t, 1, 1, 999> v491 =
+        emitc::tosa::reshape<Tensor<int64_t, 1, 1, 999>>(v489);
+    Tensor<int64_t, 1, 1, 999> v492 =
+        emitc::broadcast_in_dim<Tensor<int64_t, 1, 1, 999>>(v490, {0, 1, 2});
+    Tensor<bool, 1, 1, 999> v493 =
+        emitc::tosa::greater_equal<Tensor<bool, 1, 1, 999>>(v492, v491);
+    Tensor<int32_t, 1, 1, 999> v494 =
+        emitc::tosa::cast<Tensor<int32_t, 1, 1, 999>>(v493);
+    Tensor<int32_t, 1, 1> v495 =
+        emitc::tosa::reduce_sum<Tensor<int32_t, 1, 1>,
+                                Tensor<int32_t, 1, 1, 999>>(v494, 2);
+    Tensor<int32_t, 1, 1, 1> v496 =
+        emitc::tosa::reshape<Tensor<int32_t, 1, 1, 1>>(v495);
+    Tensor<int32_t, 1, 1> v497 =
+        emitc::tosa::reshape<Tensor<int32_t, 1, 1>>(v496);
+    Tensor<float, 1, 1> v498 = emitc::tosa::cast<Tensor<float, 1, 1>>(v497);
+    Tensor<float, 1, 1> v499 = emitc::tosa::mul(v498, v40);
+    Tensor<float, 1, 1> v500 = emitc::tosa::mul(v499, v499);
+    Tensor<float, 1, 2> v501 =
+        emitc::tosa::concat<1, Tensor<float, 1, 2>>(v499, v500);
+    Tensor<int64_t, 999> v502 = emitc::tosa::cast<Tensor<int64_t, 999>>(v46);
+    Tensor<int64_t, 1, 1, 1> v503 =
+        emitc::tosa::reshape<Tensor<int64_t, 1, 1, 1>>(v31);
+    Tensor<int64_t, 1, 1, 999> v504 =
+        emitc::tosa::reshape<Tensor<int64_t, 1, 1, 999>>(v502);
+    Tensor<int64_t, 1, 1, 999> v505 =
+        emitc::broadcast_in_dim<Tensor<int64_t, 1, 1, 999>>(v503, {0, 1, 2});
+    Tensor<bool, 1, 1, 999> v506 =
+        emitc::tosa::greater_equal<Tensor<bool, 1, 1, 999>>(v505, v504);
+    Tensor<int32_t, 1, 1, 999> v507 =
+        emitc::tosa::cast<Tensor<int32_t, 1, 1, 999>>(v506);
+    Tensor<int32_t, 1, 1> v508 =
+        emitc::tosa::reduce_sum<Tensor<int32_t, 1, 1>,
+                                Tensor<int32_t, 1, 1, 999>>(v507, 2);
+    Tensor<int32_t, 1, 1, 1> v509 =
+        emitc::tosa::reshape<Tensor<int32_t, 1, 1, 1>>(v508);
+    Tensor<int32_t, 1, 1> v510 =
+        emitc::tosa::reshape<Tensor<int32_t, 1, 1>>(v509);
+    Tensor<float, 1, 1> v511 = emitc::tosa::cast<Tensor<float, 1, 1>>(v510);
+    Tensor<float, 1, 1> v512 = emitc::tosa::mul(v511, v40);
+    Tensor<float, 1, 1> v513 = emitc::tosa::mul(v512, v512);
+    Tensor<float, 1, 2> v514 =
+        emitc::tosa::concat<1, Tensor<float, 1, 2>>(v512, v513);
+    Tensor<int64_t, 999> v515 = emitc::tosa::cast<Tensor<int64_t, 999>>(v45);
+    Tensor<int64_t, 1, 1, 1> v516 =
+        emitc::tosa::reshape<Tensor<int64_t, 1, 1, 1>>(v3);
+    Tensor<int64_t, 1, 1, 999> v517 =
+        emitc::tosa::reshape<Tensor<int64_t, 1, 1, 999>>(v515);
+    Tensor<int64_t, 1, 1, 999> v518 =
+        emitc::broadcast_in_dim<Tensor<int64_t, 1, 1, 999>>(v516, {0, 1, 2});
+    Tensor<bool, 1, 1, 999> v519 =
+        emitc::tosa::greater_equal<Tensor<bool, 1, 1, 999>>(v518, v517);
+    Tensor<int32_t, 1, 1, 999> v520 =
+        emitc::tosa::cast<Tensor<int32_t, 1, 1, 999>>(v519);
+    Tensor<int32_t, 1, 1> v521 =
+        emitc::tosa::reduce_sum<Tensor<int32_t, 1, 1>,
+                                Tensor<int32_t, 1, 1, 999>>(v520, 2);
+    Tensor<int32_t, 1, 1, 1> v522 =
+        emitc::tosa::reshape<Tensor<int32_t, 1, 1, 1>>(v521);
+    Tensor<int32_t, 1, 1> v523 =
+        emitc::tosa::reshape<Tensor<int32_t, 1, 1>>(v522);
+    Tensor<float, 1, 1> v524 = emitc::tosa::cast<Tensor<float, 1, 1>>(v523);
+    Tensor<float, 1, 1> v525 = emitc::tosa::mul(v524, v40);
+    Tensor<float, 1, 1> v526 = emitc::tosa::mul(v525, v525);
+    Tensor<float, 1, 2> v527 =
+        emitc::tosa::concat<1, Tensor<float, 1, 2>>(v525, v526);
+    Tensor<float, 1, 71> v528 = emitc::tosa::concat<1, Tensor<float, 1, 71>>(
+        v334, v462, v475, v488, v501, v514, v527, v95, v108, v121, v134, v145,
+        v158, v171, v184, v197, v210, v221, v74, v234, v247, v258, v271, v282,
+        v295, v308, v321, v347, v360, v373, v386, v399, v412, v423, v436, v449);
+    Tensor<float, 1, 40> v529 =
+        emitc::tosa::fully_connected<Tensor<float, 1, 40>>(v528, v75, v76);
+    Tensor<float, 1, 40> v530 =
+        emitc::tosa::clamp(v529, (float)0.0e+00, (float)3.402823470e+38);
+    Tensor<float, 1, 40> v531 =
+        emitc::tosa::fully_connected<Tensor<float, 1, 40>>(v530, v77, v78);
+    Tensor<float, 1, 40> v532 =
+        emitc::tosa::clamp(v531, (float)0.0e+00, (float)3.402823470e+38);
+    Tensor<float, 1, 20> v533 =
+        emitc::tosa::fully_connected<Tensor<float, 1, 20>>(v532, v79, v80);
+    Tensor<float, 1, 20> v534 =
+        emitc::tosa::clamp(v533, (float)0.0e+00, (float)3.402823470e+38);
+    Tensor<float, 1, 2> v535 =
+        emitc::tosa::fully_connected<Tensor<float, 1, 2>>(v534, v81, v82);
+    Tensor<int64_t, 1> v536 =
+        emitc::tosa::argmax<Tensor<int64_t, 1>, Tensor<float, 1, 2>>(v535, 1);
+    Tensor<int64_t, 1> v537 = emitc::tosa::minimum(v536, v43);
+    Tensor<int64_t, 1> v538 = emitc::tosa::maximum(v537, v44);
+    return v538;
+  }
+};
diff --git a/llvm/lib/Passes/PassBuilderPipelines.cpp b/llvm/lib/Passes/PassBuilderPipelines.cpp
--- a/llvm/lib/Passes/PassBuilderPipelines.cpp
+++ b/llvm/lib/Passes/PassBuilderPipelines.cpp
@@ -141,7 +141,9 @@
                clEnumValN(InliningAdvisorMode::Development, "development",
                           "Use development mode (runtime-loadable model)"),
                clEnumValN(InliningAdvisorMode::Release, "release",
-                          "Use release mode (AOT-compiled model)")));
+                          "Use release mode (AOT-compiled model)"),
+               clEnumValN(InliningAdvisorMode::EmitC, "emitc",
+                          "Use EmitC-compiled model")));
 
 static cl::opt<bool> EnableSyntheticCounts(
     "enable-npm-synthetic-counts", cl::Hidden,
diff --git a/llvm/test/Transforms/Inline/ML/ml-test-emitc-mode.ll b/llvm/test/Transforms/Inline/ML/ml-test-emitc-mode.ll
new file mode 100644
--- /dev/null
+++ b/llvm/test/Transforms/Inline/ML/ml-test-emitc-mode.ll
@@ -0,0 +1,10 @@
+; The default inliner doesn't elide @adder, it believes it's too costly to inline 
+; adder into switcher. The ML inliner carries out that inlining, resulting in
+; a smaller result (part of it is that adder gets elided).
+;
+; This test uses Inputs/test-module.ll, as it will share it with a similar test
+; for the 'development' and 'release' mode.
+;
+; REQUIRES: llvm_inliner_model_emitc
+; RUN: opt -passes=scc-oz-module-inliner -enable-ml-inliner=emitc -S < %S/Inputs/test-module.ll 2>&1 | FileCheck %S/Inputs/test-module.ll --check-prefix=CHECK
+; RUN: opt -passes=scc-oz-module-inliner -enable-ml-inliner=default -S < %S/Inputs/test-module.ll 2>&1 | FileCheck %S/Inputs/test-module.ll --check-prefix=DEFAULT
diff --git a/llvm/test/lit.cfg.py b/llvm/test/lit.cfg.py
--- a/llvm/test/lit.cfg.py
+++ b/llvm/test/lit.cfg.py
@@ -321,6 +321,9 @@
 if config.llvm_inliner_model_autogenerated:
     config.available_features.add("llvm_inliner_model_autogenerated")
 
+if config.llvm_inliner_model_emitc:
+    config.available_features.add("llvm_inliner_model_emitc")
+
 if config.llvm_raevict_model_autogenerated:
     config.available_features.add("llvm_raevict_model_autogenerated")
 
diff --git a/llvm/test/lit.site.cfg.py.in b/llvm/test/lit.site.cfg.py.in
--- a/llvm/test/lit.site.cfg.py.in
+++ b/llvm/test/lit.site.cfg.py.in
@@ -56,6 +56,7 @@
 config.have_tf_aot = @LLVM_HAVE_TF_AOT@
 config.have_tflite = @LLVM_HAVE_TFLITE@
 config.llvm_inliner_model_autogenerated = @LLVM_INLINER_MODEL_AUTOGENERATED@
+config.llvm_inliner_model_emitc = @LLVM_HAVE_EMITC_INLINERSIZEMODEL@
 config.llvm_raevict_model_autogenerated = @LLVM_RAEVICT_MODEL_AUTOGENERATED@
 config.expensive_checks = @LLVM_ENABLE_EXPENSIVE_CHECKS@
 config.dxil_tests = @LLVM_INCLUDE_DXIL_TESTS@